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by Sean Hess (email@example.com) 904-386-8327 , Broker and Manager for St. Augustine Team Realty (www.StAugustineTeamRealty.com). Join us on Facebook.
Author's note: This is a selection from my new book, How To Buy A Home and Not Lose Sex: Find the Best House, Make the Best Offer, and Keep Your Love Life, which you can purchase on Amazon by clicking here.
In the next few weeks I'm going to cover all the closing costs I can think of. In past weeks I've covered the down payment, the cost of a Realtor, earnest money deposits, and prepaid closing costs. This week I'm going to hit flood elevation certificates, insurance, and escrows. Oh, and freaking out about closing costs too!
Flood Certs and Homeowners Insurance
I'll cover flood-elevation certs first.
For many homes (or a first-floor condo or townhome) near water, rivers, lakes, streams, creeks, swamps, and the ocean, even if the body of water doesn't exist anymore, you might need a flood-elevation certificate ("flood cert") to get an insurance quote. And you need insurance if you want a loan.
You will probably need the flood cert before you can even shop for insurance. Insurance companies almost won't talk to you (at least not in Florida) if you don't have a flood cert.
Once you've decided on your insurance company, you can "bind" the insurance by paying for it immediately in full. You always have to pay for a full year of insurance up front, but you can choose to do it right away or wait until closing.
But understand, whether you do it now or do it at closing, you have to pay for a full year of homeowner's insurance up front.
And this full year does not include the insurance escrows, which I'll cover in the next section.
Quick tip: It's a good idea to have a survey done at the same time as the flood cert because they are both done by surveyors; if you do them separately, it will cost more because the surveyor has to come out twice.
Sometimes a survey is required by a bank to get a loan, and sometimes the bank will accept an old one. It can be a really good idea to have a new survey done early in the process so you know exactly where fences and buildings sit on a lot, just in case something is over the line and needs to be dealt with—especially if the existing survey is several years old, and fences or outbuildings have since been added.
Escrows? What the Hell Are Those?
Tax and insurance escrows are the time bombs of closing costs.
They are easy to miss.
While your lender will include escrows on your monthly payment estimate, they are often forgotten or overlooked for the closing-costs estimate.
This can lead to sticker shock on the day of closing.
I'll explain.
Your monthly mortgage payment will have one month's worth of property taxes and one month's worth of homeowner's insurance tacked on.
The money for taxes is put into one escrow account. The money for insurance is put into another escrow account.
An escrow account is basically a holding account. It has one purpose: to hold enough money to pay your taxes or insurance when they are due.
At tax time, or when your insurance bill is due, these escrow accounts will have enough money to pay the bill. You don't have to remember to pay it. You don't have to write a separate check. It happens automatically.
Here's where the time bomb comes in.
At closing, they will charge you two to four months' worth of taxes, and two to four months' worth of insurance, to "build up" these accounts for the first time.
If the taxes are $4,000 a year, three months' worth of taxes would be $1,000. If you weren't preparing to pay that extra thousand at closing, or if you weren't aware of it, you are going to freak out a bit. Many people do.
So if you are heading into a real estate closing, take a good look at your closing-costs estimates. Find the tax and insurance escrow estimate. If you don't see it, call your lender.
Note: Starting on August 1, 2015, the Loan Estimate provided when you shop mortgages will have estimates of your monthly payment and closing costs that will be fairly accurate. It will still be a "time bomb" if you put the Loan Estimate aside, however, and don't look at it until a few days before closing.
Get Your Freak On (Freaking Out About Money)
Feels like you're being fed by a fire hose right now, doesn't it?
"What the hell," you say. "I just wanted to pay for the house!"
"What are escrows?!" you continue, wide-eyed and exasperated. "Who said anything about inspections? Pests and wood rot? I need those? I am so NOT buying a house!"
It's cool. Something you need to know right now: Every buyer in every home purchase has a freak-out moment.
Have your freak out, take your mind off it for a day or two, and you'll feel better. It's all good.
If you are thinking about buying a home, please consider hiring myself and my team as your Realtors. We can help you find the best house and make the best offer. Contact me at email@example.com or my partner Kate at firstname.lastname@example.com.
Tags: Escrows buying a house, Flood certs buying a house, Flood elevation certificate when buying a house, Freaking out about closing costs, How to buy a house and keep your love life, How to buy a house and not lose sex, How to buy a house and not lose your love life, Insurance when buying a house, St Augustine Realtor how to buy a house and not lose sex


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Family Court Tool Kit: Trauma and Child Development
Moving toward a trauma-responsive, developmentally-informed court with a foundation in cutting-edge science
Trauma-related news
An exciting new website from the Centers for Disease Control and Prevention provides a free library of photos and videos that illustrate developmental milestones. This resource, entitled Milestones in Action, provides information for children age two months through five years.
Click here for Red Flags of Trauma, a quick guide for judges, developed by Judge Lynn Tepper, Sixth Judicial Circuit.
Child development news
Click here to read a January 2017 policy statement from the U.S. Department of Health and Human Services and the U.S. Department of Education. The document includes a comprehensive listing of resources beginning on page 20. The purpose of the policy statement is "to support States and communities in their efforts to better coordinate, align, and enhance health1 and early learning systems2 to promote the healthy development, early learning, and well-being of all children from birth to Kindergarten entry in the United States. This statement is intended for State and local policy-makers and administrators of systems, agencies, and programs responsible for children's health, social-emotional development, and early learning to understand their role and take steps to improve the integration of services for young children.
What is this tool kit?
This tool kit contains compelling information, rooted in science, which aids in determining children's needs based on developmental milestones and the impact of trauma.
Why use it?
The information and practices in this tool kit will improve judicial decision making and improve outcomes for children. The practices are in keeping with guiding principles from In re: Report of the Family Court Steering Committee, 794 So. 2d 518 (Fla. 2001). Federal regulations, state statutes, Florida Supreme Court opinions, and a judicial canon support these practices and authorize trauma screening and treatment. Click here for citations.
Who should use it?
Judges, magistrates, and hearing officers who preside over family court cases, as well as court partners, including but not limited to mediators, attorneys, parenting coordinators, case managers, juvenile probation officers, and clerks who handle family court cases. This includes most family court case types as specified by rule. In addition, judges hearing criminal cases and those in problem-solving courts (such as drug courts, mental health courts, and veterans courts) will also benefit from studying this tool kit.
When should it be used?
Cutting edge research in the area of brain development, attachment, and child trauma demands it.
How should it be used?
The tool kit is organized in the following way: it begins with a summary of the problem, offers a proposed solution, states
the goal, and provides practical tools.
Read it, digest it, practice it, and present it for discussion at a multidisciplinary brown bag luncheon, model court workshop, or learning circle.

subset: commoncrawl


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Khadi gold peel off mask blankets the skin helping the active ingredients penetrate. gently exfoliates your skin and removes dead skins dirt dust and pollutions from your skin. It open the skin pores and gets absorbed by the skin. Your skin will have an incredible iridescence while using Khadi gold peel off mask.The gold will dissolve on your skin's surface to instantly reflect light and tiny particles will seep into protect against fine lines and discolouration.Read more ...
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Khadi gold peel off mask contains original 24 karat gold with other herbal extracts such as aloevera rose petals Vaseline bees wax and purified water.It helps skin to tone up and removes the dead cells from face. Gold has excellent anti ageing properties and because of its bio availability ability to get absorbed by the body it creates a reservoir in the skin that fights the breakdown of collagen.Also gold content in this face mask allows the other ingredients to penetrate inside the skin. If you are looking for a healthy and younger skin you must take care of your skin regularly. Creams facial sunscreens all helps upto one extent but Using a face pack is one of the important step of a good skin regimen. Facial skins are most exposed to sun without any cover and ofcourse it needs more care and protection.


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Engage our certified experts to provide a little—or a lot of—extra help when you need it. Imagine being able to shift your focus from daily tactical administration of Cornerstone to strategic initiatives that drive your business. With Tribridge Managed Services, it's possible.
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RoadsideThoughs depends on cookies for its operation. Please refer to our
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By closing this banner or continuing to browse RoadsideThoughts, you agree to the use of cookies.
Below is our collection of tidbits, odds 'n ends for Noland.
This page contains two sections:
We spend a lot of time looking through Gazetteers that were published in the late 1800's and early 1900's. Many of the Gazetteers include the distance from a community such as Noland to various places of note, such as the White House.
With a nod to our favorite Gazetteers, the straight-line distance<1> beginning in Noland to:
We have found 4 communities that share the name Noland.
While the name Noland is unique within Alabama, we know of another 4 communities with the same name in the United States.
Communities Outside of Alabama
Randolph County
Our profile page for Noland, Randolph County [Map]Distance: About 267 miles [429.7 km] to the Northwest
Boulder County
Our profile page for Noland, Boulder County [Map]Distance: About 1,069 miles [1,720.4 km] to the West Northwest
Ford County
Our profile page for Noland, Ford CountyWe found mention of this community, but have little information.<7>Although located in Ford County, we don't know the location of this community within the county.
Estill County
Our profile page for Noland, Estill County [Map]Distance: About 372 miles [598.7 km] to the North Northeast
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Cities, Towns and Villages of Alabama
About Pickens County
About Alabama
Unfortunately, we don't know of a website for Noland. If you can help, please contact us through our Feedback Page.
The official website for State of Alabama: www.alabama.gov/
- Return to Citation
For additional information, please visit our Glossary for Noland.
Please keep in mind that we can not guarantee the accuracy or timeliness of the information
on this website, so use with care. We encourage you to double-check the information that is critical to you.
If you've found an error or have additional information that you would like to share,
please don't hesitate to write. Click here to contact us.


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LGBT Rights
Homosexuality is legal in Serbia. Age of Consent is fourteen (14) years.
Protection Based on Sexual Orientation and/or Gender Identity in LawsEdit
Until 2002, Serbia had no special protection on LGBT rights.[1]
Since 2003, regarding hate speech in media, there is a ban against discrimination based on sexual orientation. It was introduced in the Information Law. Additionally, the same ban existed previously in the Radio Emitters Law, adopted 2002. Unfortunately, these laws are not obeyed, and Radio Emitters Agency, an independent governmental agency that should force those bans on registered emitters, hasn't done anything so far, regardless of LGBT NGOs demands.
In 2005, through a change in the Labor Law, discrimination based on sexual orientation in employment was put out of legality. However, there are no public records that this ban was used for prosecutions so far.
Recognition of same-sex couplesEdit
While same-sex couples have never been recognized by law, the new Serbian constitution, adopted in November 2006, has an outright ban on same-sex marriages and other forms of recognition, such as domestic partnerships, civil unions, etc. The measure consists of defining a recognized and accepted relationship as only as one between a male and a female, thus limiting any same-sex couple rights.[2]
Male homosexuality in Serbia was banned by law since 1977, but there are no records that law was ever applied. Laws never addressed female homosexuality, mostly because of lawmakers' negligence on the topic.
In 1978, male homosexuality became legal in Vojvodina, the northern province of Serbia, when Serbian provinces had a certain degree of law-making power; this move complied with relaxed public opinion on this issue in the province. As of then, homosexuality was in all terms equal with heterosexuality, without any special rights on same-sex marriages, same-sex unions or protection against discrimination. The legalization lasted until 1990, when Vojvodina lost its powers, falling back to the legal system of Serbia, which was forbidding male homosexuality at that time.
Finally, in 1994 male homosexuality became legal in Serbia, with an Age of Consent of eighteen (18) for anal intercourse between males and fourteen (14) for other sexual practices. Then, an equal Age of Consent of fourteen (14) was introduced on January 1, 2006, regardless of sexual orientation and/or gender.
Military LawEdit
Homosexuals are banned from military service; however proving that an individual is a homosexual is uncommon since the process for doing so is very difficult and embarrassing, with numerous psychological sessions every couple of years. The Serbian army goes by a Don't ask, don't tell policy similar to the one in the United States armed forces.
Development of activismEdit
Development of LGBT rights and culture in Serbia is contributed by LGBT sites such as the oldest Adriatic LGBT Activism mailing list in the region, Gay-Serbia and Queeria which are important online points for organising gay activism, as well as for meeting other gay and gay-tolerant people.
Gay life in the countryEdit
Gays and lesbians continue to face discrimination and harassment in Serbia. The majority of Serbian people display vast anti-gay attitudes. There have been numerous instances of violent gay-bashing, the most extreme during the first Belgrade Gay Pride.
There were two other plans for Pride Day celebration in Serbia, one in Belgrade in 2004 initiated by activists around GSA and another in Novi Sad initiated by LGBT Vojvodina in 2007, but because of low cooperation between activist groups and inability to provide adequate safety against violence due to limited funding, they never made it.
Official medical textbooks that classify homosexuality under "Sexual Deviations and Disorders" are widely used through. After several requests to do so, Serbian Medical Society has finally stated that same-sex orientation is not a disease in official letter to Labris in 2008.
The gay scene is small with clubs proclaiming themselves as LGBT (friendly) opening and closing frequently. As of 2008, Loud & Queer is the only official LGBT club, working in Belgrade. Additionally, few gay and gay-friendly cafes are located in the downtown area of Belgrade and Novi Sad, but their existence is a public secret.
LGBT Organizations in SerbiaEdit
- Lambda, LGBT human rights and queer culture centre (with offices in Niš and Kragujevac)
- LGBT Vojvodina, rights and culture development center of homosexual, bisexual and transsexual/transgender population of Vojvodina
- Labris, lesbian human rights group
- Taboo, group for affirmation of diversity. NGO working in Zrenjanin, Kragujevac and other smaller Serbian towns.
- Queeria
- Gayten LGBT
- Gay-straight Alliance, based in Belgrade
- Koalicija protiv diskriminacije / Coalition Against Discrimination
Summary table Edit
|Homosexuality Legal
|Equal Age of Consent; since 2006
|Anti-discrimination Laws in Employment under Labor Law
|Anti-discrimination Laws in the Provision of Goods and Services
|Anti-discrimination Laws in the Media (note: this law is frequently violated with impunity)
|Anti-discrimination Laws in all Other Areas (indirect discrimination, hate speech by individuals, others)
|Same-sex Marriages
|Recognition of Same-sex unions
|Adoption by Same-sex couples
|Gays Allowed to Serve in the Military
|Right to Change Legal Gender
|Access to IVF for Lesbians
|MSMs Allowed to Donate Blood (if had an anal intercourse in the last six months)
Template:LGBT rights in Europepl:Sytuacja prawna osób LGBT w Serbii


text: Eevee Evolutions Eevee Evolutions Shiny Pokémon Ndex 131-140 Water Types Retrieved from "http://leonhartopedia.wikia.com/wiki/Vaporeon/Products/Posters?oldid=89715" Ad blocker interference detected! Wikia is a free-to-use site that makes money from advertising. We have a modified experience for viewers using ad blockers Wikia is not accessible if you've made further modifications. Remove the custom ad blocker rule(s) and the page will load as expected. Categories: Vaporeon


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Entry requirements
Minimum upper second-class degree from a UK university, or equivalent overseas/professional qualification; applications assessed on individual merit.
Months of entry
October, September
Course content
This course is designed for students who are planning on creating their own digital business or gaining a managerial position in larger businesses which make use of digital platforms. It is suitable for students with a background in management or the social sciences. A technical background is not required.
Information for international students
Please visit our website for information for: International students
Fees and funding
Please visit our website for:
Qualification and course duration
full time
12 months
Course contact details
- Name
- Southampton Business School
- firstname.lastname@example.com
- Phone
- +44 (0)23 8059 4393


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I had lunch this week at Fez on Central. Although co-workers had been there a few times before, I had never joined them. Central Avenue is a construction mess right now, and it took us 10 minutes to travel 1 1/2 miles, but we made it.
Fez has kind of a modern, hip atmosphere. Stained concrete floor, mod but comfortable furniture and cool art. 3/4 of the space is restaurant, with the remaining 1/2 the bar area. The restaurant was full when we arrived, so we were seated at one of the 3-4 high tables in the bar. The padded seats were actually quite comfortable.
The guys were all talking about the hamburgers and the fries, so I stuck with that. The fez fries are $3.50 for each basket, and come in four flavors: cinnamon dusted sweet potato fries, spicy harissa, garlic and rosemary, and salt and pepper fries. The guys had pretty much decided on the garlic and the salt and pepper fries, so I just went along and the four of us shared two baskets, which was plenty.
The burgers are all 1/2 pound grilled angus, topped with different sauces and garnish. Since it was my first time, I ordered the fez burger which comes with spicy honey molasses bbq, cinnamon pears, feta, lemon garlic aioli, crispy onions and cilantro on a ciabatta (all the burgers are $9.95 and come with a field salad with lemon basil vinaigrette). Now let me tell you that I generally prefer a burger with lettuce, tomato, onion and a little ketchup. I am not a "trendy burger" guy. But this burger was really very good. Cooked perfectly, the combination of flavors really enhanced the flavor of the burger. They didn't load on the condiments, so nothing over powered the burger itself. I was actually very impressed.
The fries are addicting, but soon become overwhelming do to the additives. They are a medium cut, and arrived hot and perfect. The flavors are really piled on and I found I enjoyed them a lot, although it became tiring after a while. Just too much flavor and probably too much food.
This was not an inexpensive lunch. Total out the door with tip was $15 each, which is quite a bit more than we usually pay for a quick lunch, but it was good. I still prefer Delux for a burgers and fries (or even Lenny's on Central for the cheap version), but Fez is closer to my office and a pretty good option.
FEZ Restaurant & Bar
3815 N. Central Avenue
Phoenix, AZ 85012
ph: 602.287.8700


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The MSN-02GA Hell Zeong Galaxy is a mobile suit from the Gundam Build Fighters anime, and a variant of MSN-02 Zeong. The Gunpla is built and controlled by C.
Technology & Combat Characteristics
While mostly similar to the Hell Zeong Marine, the Zeong Hell Galaxy is fitted for space use and retains the forearms of the original MSN-02 Zeong. The suit also has a pair of clawed, wire-guided legs, this allows the suit to perform all-range attacks with all four limbs. The legs can also be used to grapple enemy units that are too close to the suit.
- Wire-guided 5-barrel Mega Particle Gun
- The forearms of the Hell Zeong Galaxy can be detached from the main body, they are then controlled via cables and maneuvered using thrusters built into their base. Each of the forearms are armed with powerful mega particle guns built into the fingers.
- Mega Particle Gun
- There are two fire-linked mega particle guns mounted in the waist of the mobile suit, one in the head and another two in the clawed, wire-guided legs. Combined with the 5-barrel Mega Particle Gun in each arm, the large amount of beams fired is very hard to evade.
The Hell Zeong Galaxy was controlled by a member of the Gunpla Mafia known only as C. After his first attempt to take Sei Iori and Reiji out of the competition failed, he insisted on trying again while Takeshi Iori was teaching Reiji how to build Gunpla. Once Reiji had completed his new GPB-X80 Beginning Gundam, he went to try it out against an automated opponent, only to find the Zeong Hell Galaxy waiting for him. Though initially getting the upper hand, C soon found himself outflanked when Aila Jyrkiäinen joined the battle with her new SDV-04 (SDV-04G) Command Gundam.


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Learn Earn Academy FREE "Price Analysis and Market Behaviour" workshop. 1Learn Earn Academy Mumbai (2nd -3rd Nov) @ 5 pm to 7 pm, Address: Learn Earn Academy 2nd Floor,Nanak Chambers,Opposite Fun Republic, D-7, K L Walawalkar Marg, Andheri West, Mumbai, MH 400058 Pls check the blog Learnearnacademy.wordpress.com. Call for registration Limited seats first come serve bases. 16+ Trainings• 300+ Participants PRICE ANALYSIS AND MARKET BEHAVIOUR TRAINING With Simple Theories – Simple Calculations & Price Psychology. • Its applicable anywhere in this world. • Same technique applicable anywhere based on only price. ________________________________________ "Having attended other such bootcamps by so called "Trading Guru's", this one is the BEST so far. The differentiator was the hands on experience using of simple trading system." — Vaibhav "Taking this workshop was truly an enlightening experience. The trainers manner of imparting knowledge and information was like getting consulted by a friend. The take aways from the excellent content and the very good quality of speakers." — Sunita Alternatively, you can drop us an email on email@example.com or call us on +91-9870531428. ________________________________________ Key Points Workshop 1. How to trade in Market? 2. Why its easy to trade in Market 3. How you can be Financially Independent Using this System. ________________________________________ About Us •We incorporated in 12 Dec 2012 as an private limited firm & we aspire to be India's best training Company the future years. • Our company is one of the genuine training companies in India, which provides training to all kind of people. Our team has developed the best trading technique which is very simple and fast to learn. Our Vision • To make every individual self employed and increase their standard of living. We train people to become knowledgeable in trading and money management. • We are strongly committed to the education of our members, so we believe in "Learn & Earn"concept. • Our training is designed by professionals to teach our members, how to trade the Indian market (Stock / Commodity & Forex Market) and generate fair revenue for themselves About the Trainers •Well qualified with good years of direct experience in financial Industry. •Hands-on experience in trading. The trainers have used the same system extensively. •Adept at training people, understanding body language and giving personal attention. Who Should Attend this Workshop •Anyone can with or without experience •Professionals, businessmen, service people, pensioners,housewives & anyone above 18 years age. The Training Room. We conduct our trainings at the state of the art training facility. Talk to our experts to earn Part/Full time income from this training. Do you want to Break Free? Come and Experience the Joy of Financial Independence.
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Online Learned Discriminative Part-Based Appearance Models for Multi-Human Tracking
- Elfreda Cain
- 2 years ago
- Views:
1 Online Learned Discriminative Part-Based Appearance Models for Multi-Human Tracing Bo Yang and Ram Nevatia Institute for Robotics and Intelligent Systems, University of Southern California Los Angeles, CA 90089, USA Abstract. We introduce an online learning approach to produce discriminative part-based appearance models (DPAMs) for tracing multiple humans in real scenes by incorporating association based and category free tracing methods. Detection responses are gradually associated into traclets in multiple levels to produce final tracs. Unlie most previous multi-target tracing approaches which do not explicitly consider occlusions in appearance modeling, we introduce a part based model that explicitly finds unoccluded parts by occlusion reasoning in each frame, so that occluded parts are removed in appearance modeling. Then DPAMs for each traclet is online learned to distinguish a traclet with others as well as the bacground, and is further used in a conservative category free tracing approach to partially overcome the missed detection problem as well as to reduce difficulties in traclet associations under long gaps. We evaluate our approach on three public data sets, and show significant improvements compared with state-of-art methods. Key words: multi-human tracing, online learned discriminative models 1 Introduction Tracing multiple targets in real scenes remains an important topic in computer vision. Most previous approaches can be classified into Association Based Tracing (ABT) or Category Free Tracing (CFT); ABT is usually a fully automatic process to associate detection responses into tracs, while CFT usually tracs a manual labeled region without requirements of pre-trained detectors. This paper aims at incorporating merits of both ABT and CFT in a unified framewor. A ey aspect of our approach is online learning discriminative part-based appearance models for robust multi-human tracing. Association based tracing methods focus on specific inds of objects, e.g., humans, faces, or vehicles [1 5]; they use a pre-trained detector for the concerned ind of objects to produce detection responses, then associate them into traclets, i.e., trac fragments, and produce final tracs by lining the traclets in one or multiple steps. The whole process is typically fully automatic. On the contrary, category free tracing methods, sometimes called visual tracing in previous wor, continuously trac a region based on manual labels in the first frame without requirements of pre-trained detectors [6 8].
2 2 Bo Yang and Ram Nevatia Frame 664 Frame 685 Frame 706 Fig. 1. Limitations of previous association based tracing methods. See text for details. initialization trac solution motion cue association based tracing auto & imperfect global available category free tracing manual & perfect individual unavailable Table 1. Comparison of association based tracing with category free tracing. We focus on automatically tracing multiple humans in real scenes. As humans may enter or exit scenes frequently, manual initialization is impractical. Therefore, association based tracing is frequently adopted in previous wor for this problem [1 4]. In the ABT framewor, lining probabilities between traclet pairs are often defined as P lin (T i T j ) = P a (T i T j )P m (T i T j )P t (T i T j ) (1) where P a ( ), P m ( ), and P t ( ) denote appearance, motion, and temporal lining probabilities. P t ( ) is often a binary function to avoid temporally overlapped traclets to be associated, and designs of P a ( ) and P m ( ) play an important role in performance. A global optimal lining solution for all traclets is often found by a Hungarian algorithm [9, 10] or networ flow methods [1, 4]. However, in most previous ABT wor, occlusions are not explicitly considered in appearance models for calculating P a ( ), leading to a high lielihood that regions of one person are used to model appearances for another. In addition, ABT only fill gaps between traclets but does not extend them; hence missed detections at the beginning or end of a traclet cannot be corrected for associations. An example is shown in Figure 1; the man in blue is missed from frame 664 until frame 685 due to failure of detectors. Moreover, to compute P m ( ) in Equ. 1, it is often assumed that humans move linearly with a stable speed; this assumption would be problematic for long gaps. For example, in Figure 1, person 29 in frame 664 and person 28 in frame 706 are actually the same person but his trac is fragmented due to the failure of the detector during the direction change. One possible solution to overcoming the detection limitation is to use category free tracing methods. However, it is difficult to directly use CFT in association based tracing due to differences in many aspects, as shown in Table 1. First, CFT starts from a perfect manually labeled region; however, in our problem, automatic initialization is provided by an pre-learned detector, and some detections may be imprecise or false alarms. Second, CFT methods often find best solutions for one or few targets individually; however, in multi-target tracing, a global solution for all targets is more important. Finally, CFT often
3 Online Learned Part-Based Models for Multi-Human Tracing 3 Video Frames Pedestrian Detector Low Level Association Legend Traclet Detection Response Select Traclets for Category Free Tracing Occlusion Reasoning for Human Parts Head Tail Tail Tail +1-1 Learning Appearance Model 1 Collect Training Samples for Category Free Tracing Head +1-1 Learning Appearance Model 2 Conservative Category Free Tracing +1-1 Motion Cues Collect Training Samples from All Traclets Learning Global Appearance Model Traclets Association Fig. 2. Tracing framewor of our approach. Colors of detection responses come from their traclets colors, and blac circles denote samples extracted from bacgrounds. Head or tail are the earliest or latest parts of a traclet. Best viewed in color. ignores motion cues, to deal with abrupt motion, while most multi-human tracing problems focus on surveillance videos where people are unliely to change motion directions and speeds much in a short period, e.g., 4 or 5 frames. We propose a unified framewor that finds global tracing solutions while incorporating the merits of category free tracing with little extra computational cost. We introduce the online learned Discriminative Part-based Appearance Models (DPAMs) to explicitly deal with occlusion problems and detection limitations. The system framewor is shown in Figure 2. A human detector is applied to each video frame, and detection responses in neighboring frames are conservatively associated into traclets. Then based on occlusion reasoning for human parts, we select traclets that are reliable for CFT. DPAMs for each traclet are online learned to differentiate the traclet from bacgrounds and other possibly close-by traclets. A conservative CFT method is introduced to safely trac reliable targets without detections, so that missing head or tail parts of traclets are partially recovered and gaps between traclets are reduced maing linear motion estimations more precise. Finally a global appearance model is learned, and traclets are associated according to appearance and motion cues. We emphasize that the category free tracing module in our framewor is not proposed to find specific targets from entry to exit as most existing CFT methods do, but is used for extrapolating the traclets and enabling associations to be made more robustly. The contributions of this paper are: A unified framewor to utilize merits of both ABT and CFT. Part based appearance models to explicitly deal with human inter-occlusions. A conservative category free tracing method based on DPAMs. The rest of the paper is organized as follows: Section 2 discusses related wor; building part-based feature sets is described in Section 3; Section 4 introduces the online learning process of DPAMs and the conservative CFT method; experiments are shown in Section 5, followed by conclusion in Section 6.
4 4 Bo Yang and Ram Nevatia 2 Related Wor Tracing multiple humans has attracted much attention from researchers. To deal with large number of humans, many association based tracing approaches have been proposed [11, 5, 2]. These approaches detect humans in each frame and gradually associate them into tracs based on motion and appearance cues. Appearance models are often pre-defined [9, 4] or online learned [1, 12, 13]. Occlusions are often ignored [13, 2] or modeled as potential nodes in an association graph [4, 1], but have not been used explicitly for appearance modeling, indicating high possibilities that parts used for modeling appearances of a person belong to other individuals. Moreover, performance of ABT is constrained by detection results; missed detections in the beginning or ends of a trac cannot be recovered, and traclets with long gaps between them are difficult to associate according to the commonly used linear motion model [9, 14, 10]. Category free tracing methods do not depend on pre-learned detectors, and appearance models are often based on parts to deal with partial occlusions [6 8]. However, CFT focuses on single or a few objects individually without finding a global solution for all targets, and is difficult to deal with large number of targets due to high complexity. In addition, CFT methods are often sensitive to initialization; once the tracing drifts, it is difficult to recover. If the initialization is imprecise or even a false alarm, the proposed tracs would be problematic. Note that some previous wor also adopts CFT techniques into multi-target tracing problems [9, 3, 15]. However, CFT is often used to generate initial traclets, which may include many false traclets or miss some traclets without carefully tuning the initialization thresholds for each video. However, we use conservative CFT methods to reduce association difficulties and missed detections in an association based framewor, but we do not rely it on finding whole tracs. Some previous wor also uses parts in tracing [16, 17]; however, only separated persons with few inter-occlusions are considered in [16] while our scenarios are more crowded with frequent occlusions. Parts in [17] are used for detection but not for appearance modeling in tracing as we did. 3 Building Part-Based Feature Sets for Traclets In order to improve efficiency, we trac in sliding windows one by one instead of processing the whole video at one time. In the following, we will use the term current sliding window to refer to the sliding window being processed. Given the detection responses in the current sliding window, we adopt the low level association approach as in [13] to generate reliable traclets from consecutive frames. These traclets are then associated in multiple steps. An appearance model for a traclet T includes two parts: a set of features F = {f 1, f 2,..., f n } and a set of weights W = {w 1, w 2,..., w n }. The features could be color or shape histograms extracted from some regions of responses in the traclet, and there is a unique F for each T. The set of weights measures importance of features and is often shared between multiple traclets. We will use the term appearance models to represent the set of weights for clarity. Given W, the appearance similarity between two feature sets F j and F is defined
5 Online Learned Part-Based Models for Multi-Human Tracing 5 24 x x y y (a) (b) (c) Fig. 3. Illustrations for human parts: (a) parts definition for a normalized human detection response; 15 squares are used to build part-based appearance models, their sizes are shown in blue numbers; (b)(c) show automatic occlusion inference results in real scenes; visible parts for each person are labeled with the same color, and occluded parts are not shown and not used for appearance modeling. Best viewed in color. as i w ih i (fj i, f i), where h i( ) is an evaluation function for two features, e.g., Euclidean distances or correlation coefficient between two vectors. A traclet T is defined as l detected or interpolated responses in consecutive frames from time t 1 to tl as T = {d 1,..., dl }, where l is the length of T. We produce two feature sets F head and F tail, including features modeling appearances of T s head and T s tail, i.e., the earliest or latest parts of T, respectively. Appearance lining probability from T to T j is evaluated on F tail and Fj head using a set of appropriate weights W. In order to explicitly consider occlusions, features are extracted from parts instead of from whole human responses. As shown in Figure 3(a), we use 15 parts to represent a human so that the parts are not too large to model occlusions and not too small to include meaningful features. Each response d i is normalized into and is a union of the 15 parts defined as d i = {ri (1), ri (2),..., ri (15)}. Each r i (u) is a set of features extracted from part u; for example, ri (1) include the color histogram or the hog feature of part 1. If part u is occluded, all features in r i (u) are invalid, as they may not come from the concerned human. In this section, we aim at building F head and F tail for each traclet T by explicitly considering human inter-occlusions. Scene occluders, e.g., screens, pillars, are not considered due to difficulty of inferring them. We assume that all persons stand on the same ground plane, and the camera loos down towards the ground, which is valid for typical surveillance videos. Two scene examples are shown in Figure 3(b)(c). Such assumptions indicate that smaller y-coordinate 1 in a 2D frame corresponds to larger depth from the camera in 3D. Therefore, given detection responses in one frame, we sort them according to their largest y-coordinates indicating their ground plane positions, from largest to smallest. Then we do occlusion reasoning for each person. If more than 30% of a part is occupied by parts of persons who have larger y-coordinates, 1 See Figure 3(b)(c) for definition of y-coordinates.
6 6 Bo Yang and Ram Nevatia Algorithm 1 The algorithm of building head feature set for one traclet. Input: traclet T. Initialize D head = φ and S head (j) = φ j {1,..., 15} For i = t 1,..., t l do: For j = 1,..., 15 do: If S head (j) < δ and r(j) i is unoccluded, S head (j) = S head (j) {r(j)}; i If at least one of r(j) i is unoccluded, D head = D head {d i } If j {1,..., 15}, S head (j) = δ, brea; Compute each feature in F head by averaging corresponding features in S head. Output: F head. it is labeled as occluded; otherwise, it is unoccluded. Some examples of automatic detected unoccluded parts are shown in Figure 3(b)(c) in colored squares. To produce F head for traclet T, we decompose the set into 15 subsets as F head = {F head (1), F head (2),..., F head (15)} (2) Each F head (j) is a subset that contains features only extracted from part j. To suppress noise, each feature is taen as the average of valid features from the first δ responses in T, where δ is a control parameter and is set to 8 in our experiments. For each part j we introduce a set S head (j), which contains multiple r i (j)s, i.e., features from part j, from different responses, so that each feature in F head (j) is taen as the average value of corresponding features in S head (j). Meanwhile, we also maintain a set D head, which contains all responses used in building F head ; this set is used in the learning process for appearance models. For example, D head may include the first, second, and eighth responses in traclet T. Algorithm 1 shows the process of building F head, where S head (j) denotes the number of elements in S head (j), and S head = j S head (j); the process of building F tail is similar. Our algorithm tends to find the earliest δ unoccluded regions for each part. If a part is occluded in early frames, we use later unoccluded ones to represent its appearance, assuming that the occluded parts have similar appearances with those temporally nearest unoccluded ones; this is a widely used assumption in CFT wor [18, 8]. If a part j is occluded in the whole traclet, (j) would be an empty set, as all features in it are invalid. F head 4 Online Learning DPAMs for Conservative Category Free Tracing In this section, we introduce conservative category free tracing methods, so that traclets are extended from heads or tails to partially overcome the missed detection problem, and long gaps between traclets are shortened to mae linear motion assumptions more reliable. 4.1 Learning of Discriminative Part-Based Appearance Models As category free tracing in our framewor is a conservative process and we do not heavily rely on it to find whole trajectories, we care more about precision
7 Online Learned Part-Based Models for Multi-Human Tracing 7 T 1 +1 T tail p (a) T 2 p tail +v tail t Tail Fig. 4. Online learning of appearance models for category free tracing: (a) estimation of potential distracters for T ; (b) online collected samples for learning DPAMs. than recall. Therefore, we only do CFT for reliable traclets, which satisfy three constraints: 1) they are longer than a threshold ζ, as short traclets are more possible to be false alarms; 2) the number of non-empty feature set defined in Equ.2 is larger than a threshold β, as appearance models may be unreliable if there are not enough unoccluded parts; 3) it does not reach the boundary of current sliding window. We set ζ = 10 and β = 6 in our experiments. If T qualifies for CFT from its tail, we online learn discriminative part-base appearance models (DPAMs), so that the learned models well distinguish T with other close-by traclets as well as the bacground. Far away traclets are not worth considering as it is difficult to confuse T with them. A linear motion model is often used in association based tracing wor. As shown in Figure 4(a), if the tail of traclet T locates at position p tail, after time Δt, the human is predicted to be at position p tail + v tail Δt, where v tail is the velocity of T s tail part. Therefore, we may use the linear motion model to estimate which traclets are possibly close to T in the category free tracing process; we call these traclets distracters. As the linear motion model is probably invalid over a long period, we do category free tracing only in one second long intervals. If the CFT does not meet termination conditions (detailed later), we do the CFT again for the next one second based on re-learned distracters and appearance models. We expect that the CFT results do not locate too far from the linear estimated positions within one second. The estimated distance between T and another traclet T i at frame t (t > t l ) is defined as Dist t = (p tail (p tail (p tail + v tail + v tail + v tail (t t l )) (p head i (t t l )) p t i (t t l )) (p tail i -1 v head i (b) (t 1 i t)) if t < t1 i if t [t1 i, tl i + vi tail (t t l i i )) if t > t l i i ] (3) i where denotes the Euclidean distance, and p t i denotes the response position of traclet T i at frame t. A traclet T i is a distracter for T if it satisfies t [t 1, t l ] t 1 i t t l i i & t (t l, t l + γ] Dist t < ωh tail (4) where γ denotes the frame rate per second, ω is a weight factor, set to 2 in our experiments, and h tail denotes the height of T s tail response. Equ. 4 indicates that a distracter is a traclet that has temporal overlap with T, so that it belongs to a different human than T, and may be close to the future path of T,
8 8 Bo Yang and Ram Nevatia Algorithm 2 The learning algorithm for DPAMs. Input: training set S + and S. Initialize the weights for samples w i = 1, if 2 S + xi S+ ; w i = 1, if 2 S xi S. For t = 1,..., T do: For q = 1,..., n do: r = i wiyihq(xi)vq(xi) α q = 1 1+r ln 2 1 r Select q = argmin i w i exp( α q y i h q (x i )v q (x i )) q Set α t = α q, h t = h q, v t = v q Update sample weights w i = w i exp( α t y i h t (x i )v t (x i )), and normalize weights. Output: H(x) = T 1 α th t (x). such as T 1 and T 2 in Figure 4(a). Responses in all distracters should have low appearance similarities with responses in T ; they form a set named as Ψ tail. In addition, to better distinguish T from the bacground, we also collect a set of responses B tail from the bacground in frame t l, defined as B tail = {d l b} l [1, γ] & where d l b is in frame t l & p l b = p tail +v tail l (5) The positions of these responses are selected from possible future positions of T in the video, but the responses are extracted at the last frame of T. Therefore, as long as v tail is not zero, these responses do not belong to T and should have low appearance similarities with responses in T. Then we build positive and negative training sets S + and S for learning DPAMs for T s tail, defined as S + = {x i = (d i1, di2 ), y i = +1} d i1, di2 Dtail (6) S = {x i = (d i1, di2 j ), y i = 1} d i1 Dtail & d i2 j B tail Ψ tail (7) where D tail is the set of responses used in building F tail as shown in Algorithm 1. Some visualized examples are shown in Figure 4(b). For a training sample x i, a control function v q (x i ) is defined to measure its validity of feature q as { 1 if Φ(fq ) is not unoccluded in both responses in x v q (x i ) = i 0 otherwise (8) where Φ(f q ) denote the part (1 to 15) that a feature f q belongs to. We adopt the standard RealBoost algorithm to learn discriminative appearance models based on valid features only as shown in Algorithm 2, where h q (x) denotes the wea classifier, i.e., an evaluation function for two feature qs, e.g., Euclidean distance or correlation coefficient between two vectors, and is normalized to [ 1, 1]. We adopt features defined in [13], including color, texture, and shape information. 4.2 Conservative Category Free Tracing With online learned DPAMs, we adopt a conservative category free tracing method on each reliable traclet. The CFT is done one frame by one frame. In each frame, a set of samples are generated around the predicted response by the
9 Online Learned Part-Based Models for Multi-Human Tracing 9 tail p T p tail +v tail *1 T 1 T 2 Fig. 5. Illustrations for category free tracing process. The green dashed rectangle is predicted by the linear motion model; the orange dashed ones are samples generated around the predicted position. linear motion model as shown in Figure 5. Sizes and positions of the samples are randomly disturbed around values of the predicted response. A feature set F s for each sample is extracted to evaluate its similarity to F tail. The sample with highest score, named as d, is chosen as a potential extension. However, the potential extension is not taen, if it meets any of the following termination conditions: d goes beyond the frame boundary or the sliding window boundary. The similarity between d and F tail is smaller than a threshold θ 1. The similarity between d and any Fi head is larger than a threshold θ 2, where T i starts at the frame where d locates. In our experiments, we set θ 1 = 0.8 and θ 2 = 0.2. These constraints assures a high similarity between d and F tail and a low similarity between d and any distracters, so that the category free tracing is less liely to drift or go beyond a true association. For example, in Figure 5, T and T 2 are actually the same person, and if CFT goes beyond T 2 s head, T and T 2 cannot be associated, and additional temporal overlapped tracs would be produced for the same person. But if the CFT stops early, we may still successfully associate them. If d is accepted as an extension, we update p tail and v tail for T. However, we do not update F tail to avoid drift. This is different with most existing category free tracing approaches, where appearance models are online updated for continuous tracing. In our framewor, CFT is only used to partially overcome detection limitations and shorten gaps between traclets to mae linear motion estimation more accurate. If CFT stops early, it is still possible to fill missing gaps by the global association. But errors in CFT may cause failure of associations as discussed above. The whole CFT method is shown in Algorithm 3. After conservative category free tracing, we learn a global appearance model similar to [13] using only unoccluded parts from all traclets, and then use the Hungarian algorithm to find global optimal association results. Figure 6 shows some comparing tracing results by using or disabling the category free tracing module. We can see that person 13 in Figure 6(b) would be fragmented into two traclets without the CFT due to long time missed detections and non-linear motions. However, the conservative CFT shortens the gaps between the two traclets so that they become easy to be associated. In addition, person 10 in Figure 6(b) is achieved in frame 330 without having detection responses at that time; person 8 and 15 are similar in Figure 6(d).
10 10 Bo Yang and Ram Nevatia Algorithm 3 The CFT algorithm for one traclet from its tail. Input: A reliable traclet T for CFT, and the frame rate per second γ. Initialize max frame number for one second tracing t max = t l + γ. Initialize the appearance feature set F tail for T s tail using Algorithm 1. While T does not meet the boundary of current sliding window do: Find potential distracters in the predicted tracing path of T from t l to t max. Collect training samples from T, its distracters, and the bacground in the predicted path, and learn DPAMs for the next one second using Algorithm 2. For t = t l + 1,..., t max do: Generate samples around the predicted position p tail + v tail 1. Extract the feature set for each sample and evaluate its similarity to F tail using the learned DPAMs. Chec whether termination conditions are met. If yes, stop CFT; otherwise, add the best response d into T s tail, and update p tail + γ. Update t l, and set t max = t l Output: Updated traclet T. and v tail 5 Experiments We evaluate our approach on three public data sets: PETS 2009 [19], ETH [20], and Trecvid 2008 [2], which have been commonly used in previous multitarget tracing wor. We show quantitative and visualized comparisons with state-of-art methods. We adopt the commonly used evaluation metrics in [10, 2], including recall and precision, showing detection performance after tracing; false alarms per frame (FAF); number of trajectories in the ground truth (GT); mostly traced (MT), mostly lost (ML), and partially traced (PT), denoting ratios of tracs with successful traced parts for more than 80%, less than 20%, and others; fragments (Frag), the number of times that a ground truth trac is interupted; id switches (IDS), the number of times that a produced trac changes its matched ground truth trac. The three data sets have different resolutions, densities, and average motion speeds. However, we use the same parameter settings on all, and performances all improve compared with state-of-art methods. This indicates low sensitivity of our approach on parameters. As detection performance would influence tracing results, for fair comparisons, we use the same detection results as in [13, 10, 2] on three data sets, which are provided by authors of [2, 10]. No scene occluders are manually assigned. 5.1 Results on PETS 2009 data set We use the same PETS 2009 video clip as used in [21] for fair comparison. The target density is not high in this set. However people are frequently occluded by each other or scene occluders, and may change motion directions frequently. The quantitative comparison results are shown in Table 2. We modified the ground truth annotations from [21], and fully occluded people that appear later are still labeled as the same person. We can see that by only using part based appearance models, the MT is improved by more than 10%, and fragments are reduced by 43% compared with up-to-date results in [10]. By using category
11 Online Learned Part-Based Models for Multi-Human Tracing 11 (a) (b) Frame 306 Frame 318 Frame 330 Frame 60 Frame 70 Frame 80 (c) (d) Fig. 6. Comparisons of tracing results with or without category free tracing: (a)(c) show results without CFT, (b)(d) show results on the same sequences with CFT. free tracing, we further improve MT by 5%, and reduce fragments by 85%. This indicates our part based models are effective for modeling humans appearances, and the conservative CFT method shortens gaps between traclets so that fragmented tracs can be associated based on the linear motion model. Some visualized examples are shown in Figure 6(b). 5.2 Results on ETH data set The ETH data set [20] is captured by a pair of cameras on a moving stroller in busy street scenes. The heights of human may change significantly from 40 pixels to more than 400 pixels. The cameras shift frequently maing the linear motion model less reliable, and there are frequent full inter-occlusions due to the low camera angle. We use the same ETH sequences as in [10]. Only data captured by the left camera are used in our experiment. The quantitative results are shown in Table 3. We can see that using the part models only, MT is improved for about 8%; using category free tracing improves MT significantly for an additional 12%. In ETH data, partial and full occlusions are frequent; therefore, the part based models help build more precise
12 12 Bo Yang and Ram Nevatia Method Recall Precision FAF GT MT PT ML Frag IDS Energy Minimization [21] % 17.4% 0.0% PRIMPT [10] 89.5% 99.6% % 21.1% 0.0% 23 1 Part Model Only 92.8% 95.4% % 10.5% 0.0% 13 0 Part Model + CFT 97.8% 94.8% % 5.3% 0.0% 2 0 Table 2. Comparison of results on PETS 2009 dataset. The PRIMPT results are provided by authors of [10]. Our ground truth is more strict than that in [21]. Method Recall Precision FAF GT MT PT ML Frag IDS PRIMPT [10] 76.8% 86.6% % 33.6% 8.0% Part Model Only 77.5% 90.9% % 25.0% 8.9% Part Model + CFT 81.0% 87.8% % 12.9% 8.9% Table 3. Comparison of tracing results on ETH dataset. The human detection results are the same as used in [10], and are provided by courtesy of authors of [10]. appearance models. The category free tracing method recovers many missed detections, especially for humans who appear for only short periods, e.g., less than 40 frames, and therefore improves MT significantly. Some tracing examples are shown in Figure 6(d) and Figure 7(a). Person 10 in Figure 7(a) is detected from frame 151 until frame 158, but we start to trac her from frame 140 until frame 162. After frame 162, our conservative CFT stops tracing due to large appearance changes caused by the shadows. 5.3 Results on Trecvid 2008 data set Trecvid 2008 is a very difficult data set, which contains 9 video clips with 5000 frames for each. The videos are captured in a busy airport; the density is very high, and people occlude each other frequently. Quantitative comparison results are shown in Table 4. Compared with [10], using only part based models reduces fragments and id switches by about 11% and 13% respectively; using CFT additionally reduces fragment and ID switches by about 2% and 3%. In Trecvid 2008 data sets, most improvements come from part based models and less from the category free tracing method, which is quite different with results on PETS 2009 and ETH. This is because Trecvid 2008 is a very crowded data set; in the CFT process, there are often many distracters for each traclet. Therefore the CFT often stops early because the probability is quite high that at least one of many distracters would have similar appearances with the concerned person. Figure 7(b)(c) show some tracing examples. We see that person 28 & 29 in Figure 7(b) and person 121 in Figure 7(c) are under heavy occlusions due to high densities of humans; however, our approach is able to find correct discriminative part-based appearance models, and therefore tracs these persons successfully. 5.4 Computational Speed The processing speed is highly related with the number of humans in videos. We implement our approach using C++ on a PC with 3.0GHz CPU and 8GB memory. The average speeds are 22fps, 10fps, and 6fps on PETS 2009, ETH, and Trecvid 2008 respectively. Compared with [10], which reported 7fps on Trecvid 2008, our approach does not impose much extra computational cost 2. The major 2 Detection time is not included in both our reported speed and that in [10]
13 Online Learned Part-Based Models for Multi-Human Tracing Method Recall Precision FAF GT MT PT ML Offline CRF Tracing [2] 79.2% 85.8% % 16.9% 4.9% OLDAMs [13] 80.4% 86.1% % 19.3% 4.6% PRIMPT [10] 79.2% 86.8% % 17.7% 5.2% Part Model Only 78.7% 88.2% % 20.9% 6.1% Part Model + CFT 79.2% 87.2% % 18.6% 5.9% Frag IDS Table 4. Comparison of tracing results on Trecvid 2008 dataset. The human detection results are the same as used in [2, 13, 10], and are provided by authors of [2]. (a) Frame 140 Frame 151 Frame 162 Frame 174 Frame 545 Frame 560 Frame 575 Frame 590 Frame 3185 Frame 3220 Frame 3270 Frame 3310 (b) (c) Fig. 7. Tracing results of our approach on ETH and Trecvid data sets. extra cost is from the feature extractions for all samples in the CFT module, which could be constrained by setting the number of evaluated samples. 6 Conclusion We introduced online learned discriminative part-based appearance models for multi-human tracing by incorporating merits of association based tracing and category free tracing. Part-based models are able to exclude occluded regions in appearance modeling, and a conservative category free tracing can partially overcome limitations of detection performance as well as reduce gaps between traclets in the association process. Experiments on three public data sets show significant improvement with little extra computational cost. Acnowledgments. Research was sponsored, in part, by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Govern-
14 14 Bo Yang and Ram Nevatia ment. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. References 1. Shitrit, H.B., Berclaz, J., Fleuret, F., Fua, P.: Tracing multiple people under global appearance constraints. In: ICCV. (2011) 2. Yang, B., Huang, C., Nevatia, R.: Learning affinities and dependencies for multitarget tracing using a crf model. In: CVPR. (2011) 3. Benfold, B., Reid, I.: Stable multi-target tracing in real-time surveillance video. In: CVPR. (2011) 4. Pirsiavash, H., Ramanan, D., Fowles, C.C.: Globally-optimal greedy algorithms for tracing a variable number of objects. In: CVPR. (2011) 5. Song, B., Jeng, T.Y., Staudt, E., Roy-Chowdhury, A.K.: A stochastic graph evolution framewor for robust multi-target tracing. In: ECCV. (2010) 6. Wang, S., Lu, H., Yang, F., Yang, M.H.: Superpixel tracing. In: ICCV. (2011) 7. Liu, B., Huang, J., Yang, L., Kuliows, C.: Robust tracing using local sparse appearance model and -selection. In: CVPR. (2011) 8. Grabner, H., Matas, J., Gool, L.V., Cattin, P.: Tracing the invisible: Learning where the object might be. In: CVPR. (2010) 9. Xing, J., Ai, H., Lao, S.: Multi-object tracing through occlusions by local traclets filtering and global traclets association with detection responses. In: CVPR. (2009) 10. Kuo, C.H., Nevatia, R.: How does person identity recognition help multi-person tracing? In: CVPR. (2011) 11. Pellegrini, S., Ess, A., Schindler, K., Gool, L.V.: You ll neverwal alone: Modeling social behavior for multi-target tracing. In: ICCV. (2009) 12. Stalder, S., Grabner, H., Gool, L.V.: Cascaded confidence filtering for improved tracing-by-detection. In: ECCV. (2010) 13. Kuo, C.H., Huang, C., Nevatia, R.: Multi-target tracing by on-line learned discriminative appearance models. In: CVPR. (2010) 14. Song, X., Shao, X., Zhao, H., Cui, J., Shibasai, R., Zha, H.: An online approach: Learning-semantic-scene-by-tracing and tracing-by-learning-semantic-scene. In: CVPR. (2010) 15. Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.V.: Online multi-person tracing-by-detection from a single, uncalibrated camera. IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (2011) Ramanan, D., Forsyth, D.A., Zisserman, A.: Tracing people by learning their appearance. IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (2007) Wu, B., Nevatia, R.: Detection and segmentation of multiple, partially occluded objects by grouping, merging, assigning part detection responses. International Journal of Computer Vision 82 (2009) Kalal, Z., Matas, J., Miolajczy, K.: P-n learning: Bootstrapping binary classifiers by structural constraints. In: CVPR. (2010) 19. : Pets 2009 dataset. (http://www.cvg.rdg.ac.u/pets2009.) 20. Ess, A., Bastian Leibe, K.S., van Gool, L.: Robust multiperson tracing from a mobile platform. IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (2009) Andriyeno, A., Schindler, K.: Multi-target tracing by continuous energy minimization. In: CVPR. (2011)
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In our refusal to leave well enough alone and our unwillingness to compromise, we uncovered great capability. Our engineers applied a combination of mechanical, dynamic, and mass-reducing enhancements to give our new GMC Sierra 1500 XFE better conventional engine V-8 fuel economy than any other competitor. They equipped another with America's first two-mode Hybrid technology to give the new GMC Sierra Hybrid a 40-per cent improvement in city fuel economy over the conventional engine and best-in-class V-8 fuel economy. The new GMC Sierra 3500HD, equipped with the available Duramax Diesel and Allison transmission, is still the most powerful V-8 one-ton. That's technology that happens when you engineer to exceed expectations.
Now standard on all new GMC Sierra 1500 models with Vortec 6.2L and 5.3L V-8 engines is an electronically controlled 6-speed automatic that combines high max trailering with precise control. Driver Shift Control (DSC) lets you select a range of gears to suit variable driving conditions. Shift into "M" and the currently engaged gear number is illuminated, and all other gears are accessible. As conditions vary, you may change the range of available gears by simply tap-shifting the plus/minus button up or down. The 6-speed's Tow/Haul mode reduces excessive shifting when towing or hauling a heavy load, and it automatically engages grade braking. Grade braking assists in maintaining desired speeds on downhill grades by implementing a shift schedule that utilizes the engine and transmission to slow the vehicle. Available heavy-duty cooling helps deliver improved trailer ratings on all new GMC Sierra models equipped with the 5.3L V-8 engines and the 6-speed transmission. Standard with the 4.3L V-6 and 4.8L V-8 is an electronically controlled 4-speed automatic that features overdrive for highway driving and automatic Tow/Haul mode.
The new GMC Sierra 1500 capitalizes on a wide front track and a boxed frame with high torsional rigidity. Torsional rigidity—resistance to twisting—contributes to durability and to performance. By hydro-forming the front rails, our engineers eliminated several welds, allowing for tighter packaging and improved fit at bolting points. The rear rails are fully boxed all the way back to the spare tire cross-member, where they become C-Channel. This sophisticated design helps boost payload capacity without significantly increasing vehicle weight. Ingenious hydraulic body mounts, standard on select new GMC Sierra models, securely link the cab to the frame, but in a way that isolates occupants from road (or off-road) noise and vibration. The StabiliTrak stability control system and traction control are standard on all new GMC Sierra 1500 models and select new GMC Sierra 2500HD models. The system has sensors that read the position of your steering wheel, vehicle speed, brake pressure, lateral acceleration and turning rate, then feed the data to an onboard computer.
If your new GMC Sierra is not responding adequately to your steering input, the computer adjusts engine speed and applies brake pressure to any individual wheel to help keep you on track. Its rollover-avoidance feature can help mitigate roll forces. Its sensors are programmed to proactively predict and help counteract vehicle "tip-up" on a flat road surface.


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|Nils Breunese <N.Breun...@vpro.nl>
|RE: Is it possible to gather data from various databases into another?
|Tue, 22 Feb 2011 07:49:15 GMT
How about Replication? http://wiki.apache.org/couchdb/Replication Nils. ________________________________________ Van: Andrew Stuart (SuperCoders) [firstname.lastname@example.com] Verzonden: dinsdag 22 februari 2011 0:39 Aan: firstname.lastname@example.com Onderwerp: Is it possible to gather data from various databases into another? Imagine an architecture in which there is a database per group discussion. Each user has their own private database into which data is pulled out all the various discussion databases that they have access to. Is there a mechanism in Couchdb to pull data from various databases into another one? thanks as ------------------------------------------------------------------------ VPRO www.vpro.nl ------------------------------------------------------------------------
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