BACKGROUND 
       [0001]    Video-based Automated Incident Detection AID systems are becoming increasingly adopted to monitor roadways for traffic incidents without the need for human operators. These systems, while proficient in detecting real incidents, are not so adept in discriminating these from ordinary scene changes. Examples of such problems stem from issues such as the appearance of stationary shadows on the roadway, a vehicle&#39;s headlight beam reflecting from a road sign, vehicles pushing snow aside as they move through it, and water splashing on a roadway. As a result many false alarms are often generated, lowering the accuracy of the AID system in totality. This is the result of many AID systems not having been designed to anticipate complex lighting conditions, with changing cloud cover and/or stationary objects off camera suddenly casting shadows with a change in lighting. This problem requires rectification. 
         [0002]    To combat this, a new method is needed to segment the video images into elements, marking the elements of the frame that contain static shadows. This would allow the sensitivity of the AID system to be lowered in these elements and result in fewer false alarms. This is a difficult task for many reasons. First, static shadows must be discriminated from moving shadows, the latter being shadows that are cast by vehicles traveling down the roadway. Secondly, the algorithm must be robust enough to work at all times from dawn until dusk, in spite of the changing lighting of the scene due to the position of the sun or cloud coverage. Thirdly, the method needs to work in a variety of camera placements such that manual parameter adjustment is not possible for each camera; hence, adaptive methodologies are introduced in lieu of manual calibration. Fourth, since the algorithm is expected to detect static shadows before the AID system does, the method must operate in real time and be relatively low in computational complexity. 
       SUMMARY 
       [0003]    There is provided a method for detecting environmental conditions in a traffic image sequence with emphasis on detecting static shadows on a roadway in the image sequence as well as other ambient conditions. In one method for detecting environmental conditions each image is partitioned into elements, each element comprising a set of one or more pixels which are locatable in the plane of the image. Each pixel is associated with one set of three chromatic components, and one luminance or Graylevel value. An element may be a point. An image may be a frame or an image frame. 
         [0004]    In a method for detecting environmental conditions, ambient conditions may be detected using a mixture of processes which utilize the chromatic elements, luminance qualities and temporal characteristics of the series of images. An embodiment of the method is divided into four modules which each detect and create maps for one ambient condition; one of static shadow, glare, snow and rain detection. The method may operate in real-time and uses only image information and local time to determine the environmental conditions present in the scene. 
         [0005]    For static shadow detection, a background image may be first built from a median filtered initial sequence of time-successive frames taken at sunrise time local to the camera&#39;s geographical placement. Each element in subsequent frames may be compared with its respective location in the background image to determine a change in luminance. Elements whose luminance values changed within a certain adaptive threshold may be marked as shadow candidates. Moving shadows may be filtered by use of moving object detection, and an extra filter is used to restrict the detection to roadways. Elements which pass these various tests may be determined to be static shadows. 
         [0006]    For glare detection, according to one embodiment, each element in a frame is examined to determine if its luminance is greater than a given threshold. Of these elements which pass this threshold, elements which are determined to correspond to motion are filtered from the final detection map. Elements which pass these tests are determined to be areas where glare exists in the image. 
         [0007]    For snow detection, in one embodiment, the difference is calculated between a recent background image of a scene built from a median filtered sequence of glare-subtracted time-successive frames and the next most recent background image to find significant changes in each element of the scene. The current and previous background images are both correlated to a snow sample through a similarity test and differenced to find changes to snow in the scene. This algorithm works by transforming subsections of the background images into a form which can be correlated to the snow sample images in order to determine the similarity of a pixel in the scene to snow. Thresholds are applied to the difference image and the difference of the correlation images. Elements which pass these tests are determined to be areas where moving snow exists in the image. 
         [0008]    For rain detection, each element may be examined to determine the areas in the scene where rain is present, then these rain detected areas may be examined to extract elements which have undergone recent change. 
         [0009]    Further summary of these methods for detecting environmental conditions resides in the description below, which refer at times to the annexed drawings which illustrate the methods used. 
     
     
       BRIEF DESCRIPTION OF THE FIGURES 
         [0010]    Embodiments will now be described with reference to the figures, in which like reference characters denote like elements, by way of example, and in which: 
           [0011]      FIG. 1  is a graphic representation of a RGB color space, whereby a point is described by its composition of primary color components, and represented as a vector. 
           [0012]      FIG. 2  shows a flowchart corresponding to an implementation of the main embodiment of the method; 
           [0013]      FIG. 3  shows a flowchart corresponding to an embodiment of the Static shadow detection step of  FIG. 2   
           [0014]      FIG. 4  shows a flowchart corresponding to an embodiment of the Background modeling step in the flowchart of  FIG. 3 ; 
           [0015]      FIG. 5  shows a flowchart corresponding to an embodiment of the Shadow candidates Detection step in the flowchart of  FIG. 3 ; 
           [0016]      FIG. 6  shows a flowchart corresponding to an embodiment of the Moving object detection step in the flowchart of  FIG. 3 ; 
           [0017]      FIG. 7  shows a flowchart corresponding to an embodiment of the Determine threshold step in the flowchart of  FIG. 3 ; 
           [0018]      FIG. 8  shows a flowchart corresponding to an embodiment of the Determine motion pixels based on level of difference between images step in the flowchart of  FIG. 3 ; 
           [0019]      FIG. 9  shows a flowchart corresponding to an embodiment of the Fill in areas between nearby motion pixels step in the flowchart of  FIG. 3 ; 
           [0020]      FIG. 10  shows a flowchart corresponding to an embodiment of the Filtering criteria step in the flowchart of  FIG. 1 ; 
           [0021]      FIG. 11  shows a flowchart corresponding to an embodiment of the Filtering criteria  1  step in the flowchart of  FIG. 10 ; 
           [0022]      FIG. 12  shows a flowchart corresponding to an embodiment of the Filtering criteria  2  step in the flowchart of  FIG. 10 ; 
           [0023]      FIG. 13  shows a flowchart corresponding to an embodiment of the Glare detection step of  FIG. 2 ; 
           [0024]      FIG. 14  shows a flowchart corresponding to an embodiment of the Snow detection step of  FIG. 2 ; 
           [0025]      FIG. 15  shows a flowchart corresponding to an embodiment of the Background modeling step of  FIG. 14 ; 
           [0026]      FIG. 16  shows a flowchart corresponding to an embodiment of the Background differencing step of  FIG. 14 ; 
           [0027]      FIG. 17  shows a flowchart to an embodiment of the three dimensional correlation step of  FIG. 14 . 
       
    
    
     DETAILED DESCRIPTION 
       [0028]    Various embodiments of environmental detection methods will now be described in detail with reference to the figures. The embodiments are intended to be illustrative of methods defined by the claims. 
         [0029]    The concept of identifying ambient conditions such as static shadow, glare, rain and snow in an image can be understood as the detection and marking of the presence or not of the ambient conditions at each point comprising an image. This interpretation is binary, meaning that after detection for every point in the image, an ambient condition is considered as strictly either present or absent. The information which includes the location and presence one of these ambient conditions, such as static shadows, in an image is encoded to a shadow map on a point by point basis, and can be overlaid with the original image, allowing for a simple recognition and understanding of shadowed and non-shadowed areas. 
         [0030]    The process of static shadow detection is enacted upon a time-consecutive series of images which together form a video stream. Each individual image is more commonly referred to as a frame. The view of the camera which records these images should be elevated and angled down towards the scene with a clear, unobstructed view. 
         [0031]    Each image is composed of a set of elements. The number of elements which make up a given image depends entirely on the resolution of the apparatus which recorded the scene or the sensitivity of the detection or a combination of both. In this case a digital video camera, radar image, or ultra sound source would serve as such an apparatus. 
         [0032]    The image elements themselves each reference a single location in an image (they can be overlapped or non-overlapped). Each element is attributed with a pair of coordinates and a number of pixels which allow its location to be mapped to a two-dimensional plane wherein the axes of the two dimensions are usually but not necessary orthogonal to each other. This ability to index individual pixels is crucial to video processing. 
         [0033]    Each element, also referred to as a set of pixels, is associated with chromatic components which in concert describe a single color. In the case of three colors: R, G and B form the three components and stand for the relative weight of the primary colors of Red, Green and Blue respectively. As seen in  FIG. 1 , the color of any pixel can be represented by a vector in three-dimensional space wherein each of the primary colors represents a single axis of the RGB color space. 
         [0034]    In addition to having many chromatic components, each pixel can also be represented by a single value which represents its brightness in an average form. This value, called the graylevel, is formed by taking the average of the three color components. 
         [0035]      FIG. 2  depicts the top-level embodiment of the method for detecting environmental conditions in a sequence of image frames. In  FIG. 2 , a series of four detection algorithms run to produce four distinct maps profiling the locations of each of the ambient conditions. 
         [0036]    In Step  1 , the presence or not of static shadows is detected in the scene and mapped accordingly. This step is described in more detail in the flowchart of  FIG. 3 . 
         [0037]    In  FIG. 3 , the process begins with Step  11 . Here the current time is checked against the local sunset and sunrise times, to determine what relative period of the day it is. The current time is local to the area and time-zone in which the system is deployed. Sunrise and sunset can be defined as when the upper edge of the disk of the Sun is on the horizon, considered unobstructed relative to the location of interest. These sunrise and sunset times are also local to the area and time-zone in which the system is deployed and can be stored in a user-input table which is calibrated once upon setup and deployment of the system. Due to the fact that clear static shadows are most prominent, and therefore, most detectable during periods of daylight, it is advantageous to restrict shadow detection to time between sunrise and sunset. If the current time is found to lie within this period, then it is considered to be day at that point of time and therefore performing background modeling (Step  13 ) is suitable. If the current time is found to lie outside of the sunrise and sunset times, then it is considered to be night and the processing of data is disabled until the following sunrise. In this eventuality the algorithm is diverted (Step  12 ) to wait. In Step  12 , an idle state is entered in which all processing is disabled until the time of the Sun&#39;s next rising. Ideally the process of shadow detection is meant to initialize at the time of sunrise and will produce the best results at this time, but in the event that the process is activated at some later period during the daytime, the process will still function. In Step  12  of static shadow detection process the background modeling of the scene takes place. This is done in order to produce a background image of the scene. It is advantageous that the produced background image contains no shadows therein, and hence the static shadow detection process is initialized at sunrise to generate a background image with no shadows. This step is described in more detail in the flowchart of  FIG. 4 . 
         [0038]      FIG. 4  depicts a two decision level method. In  FIG. 4 , the background modeling Step  13  begins with Step  131 , where the next frame is taken from the video stream. Normally the frame would be the frame taken immediately after sunrise, taking advantage of a common property of images taken during this period, that being that they are shadowless owing to the position of the Sun. 
         [0039]    In Step  132 , a queue of some length containing a series of sequential images is examined. If the queue is found to be full then the process proceeds to Step  134 , otherwise it proceeds to Step  133  to add another median frame, which is an unaltered frame from the video stream. 
         [0040]    According to Step  134 , a median image is computed from the median queue. Simply, this is the process of deriving a median image from a collection of sequential frames in a queue. Every element in the median image has a median R, G and B for the three color components calculated from its corresponding location in the median queue images. With a median queue of length l for every element Pi in an image with i total pixels, then calculating the median of each element can be expressed as in (1). For each individual comparison, the R, G, B values are sorted in ascending order prior to median calculation. 
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         [0041]    In Step  135 , the median queue is cleared of images in order that the process of filling it and creating a median image can be repeated. 
         [0042]    According to Step  136 , the median image created previously is added to a second queue of some length referred to as the supermedian queue. 
         [0043]    According to Step  137 , the supermedian queue is examined to determine if it is full, and in the event that it is the process continues to Step  138 . Otherwise, it proceeds to Step  131  to repeat the process of filling the median queue, calculating a new median image and adding that to the supermedian queue. 
         [0044]    According to Step  138 , a median image is computed from the frames contained in the supermedian queue using the exact same methodology as before from (1). This final image, being that it is constructed as a median of median images, is consequently referred to as a supermedian image. This image serves as the defacto background image for the method of static shadow detection, and having produced and outputted this image the background modeling step completes itself, and processing returns to the main embodiment of the method for detecting environmental conditions in a sequence of images. 
         [0045]    Returning to  FIG. 3 , once the background modeling has created a background image, Step  15  commences after the next image is drawn from the video stream in Step  14 . In Step  15  , the background image is subtracted from the current image. This is accomplished on an element by element, color component by color component basis, and this produces a color difference image. 
         [0046]    Next in Step  16  shadow candidates are selected from the current image using the color difference image, a process described in greater detail in  FIG. 5 . 
         [0047]      FIG. 5  depicts a two decision level method. In  FIG. 5  the shadow candidate detection Step  16  begins with Step  161 , where the process determines what the maximum color component change in the scene is. 
         [0048]    In Step  162 , the process checks if all pixels are processed at this point, thereby producing the shadow candidate map and completing Step  16 . Otherwise if not all pixels have been processed, then the process therefore proceeds to Steps  163  and  164 . In Step  163  the next pixel P c  is selected from the current image, from Step  14 , and in Step  164  the next element P b  is selected from the background image, from Step  13 . In Step  165 , the R, G, B color components (in the three color basis system) are extracted from P c , and in Step  166  the same three components are extracted from P b . 
         [0049]    In Step  167 , the corresponding color pairs are subtracted to produce color component differences, as in (2). 
         [0000]    
       
      
       R 
       diff 
       =R 
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       −R 
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         [0000]        G   diff   =G   c   −G   b    (2) 
         [0000]    
       
      
       B 
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         [0050]    In Step  168 , these differences are normalized with respect to the maximum change in color components computed from Step  161 . Formulaically this can be expressed as (3): 
         [0000]    
       
      
       R 
       diff,norm 
       =R 
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       max change  
      
     
         [0000]        G   diff,norm   =G   diff   /G   max change    (3) 
         [0000]    
       
      
       B 
       diff,norm 
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       max change  
      
     
         [0051]    According to Step  169 , the three resulting normalized differences from Step  168  are checked. If in all three cases the results fell within empirically-determined thresholds, the current pixel location is marked as being ‘Shadow Candidate’ (SC) on a binary map (Step  1610 ). In the event that any one or more of the comparisons failed the thresholds then the current pixel location is marked as being ‘Not Shadow Candidate’ (SC) on the binary map (Step  1611 ). 
         [0052]    Returning to  FIG. 3 , after shadow candidate detection is completed, moving object detection is carried out on the current image in Step  17 . This step is described in more detail in the flowchart of  FIG. 6 . 
         [0053]      FIG. 6  depicts a straightforward process for determining the areas in a scene where motion has occurred recently. The previous image, having been recorded, is loaded from Step  171  into Step  172 , where a threshold is derived based on differences between the current and previous images. This step is described in more detail in the flowchart of  FIG. 7 . 
         [0054]      FIG. 7  depicts a two level decision method. In  FIG. 7 , the process begins with Step  1721 , where it is checked if the pixels of the current image have all been processed. In the event that all pixels of the current image have been processed, Step  172  is complete, otherwise if not all the pixels of the current image have been processed the process proceeds to Step  1722 . Here the graylevel value of the next pixel in the current image is obtained, where graylevel value is obtained from the R, G, B values (in the three color basis system). A similar process to this is also carried out in Step  1723  with respect to the next pixel of the previous image. In Step  1724  the absolute difference between the Graylevel values of the current and previous image pixels is taken; that is, 
         [0000]      Δ=| P   c   −P   p |  (4) 
         [0055]    In Step  1725 , the process checks to see if the calculated absolute difference is the largest difference seen thus far in the current image comparison. In the event that the calculated absolute difference was the largest difference seen thus far, the module proceeds to set a new motion detection threshold value based on it in Step  1726 . In the opposite event that the calculated absolute difference was not that largest difference seen thus far, then no such threshold is calculated. In both cases the process then proceeds back to Step  1721  to either process a new pixel or complete Step  172 . 
         [0056]    Returning to  FIG. 6 , after Step  172  is completed, the motion detection process proceeds to Step  173  to determine motion pixels based on the threshold computed in Step  172 . Step  173  is described in more detail in  FIG. 8 . 
         [0057]      FIG. 8  depicts the process of marking individual pixels as either in motion or not in motion. It begins with Step  1731 , where a check is performed to see whether or not all the pixels have been processed accordingly. In the event that the pixels have all been processed, an intermediate motion detection map is returned and the process continues. Otherwise, the process proceeds next to Step  1732 . Here the graylevel value of the next pixel in the current image is obtained, where graylevel is obtained from the R, G, B values (in the three color basis system). A similar process to this is also carried out in Step  1733  with respect to the next pixel of the previous image. In Step  1734  the absolute difference between the graylevel values of the current and previous image pixels is taken as in equation (4). 
         [0058]    In Step  1735 , in the event that the absolute difference is greater than the threshold calculated in Step  172 , the pixel at this location is marked as ‘In Motion’ (IM) on the intermediate motion detection map in Step  1736 . Otherwise, the location is marked as ‘Not In Motion’ (  IM ) in Step  1737 . In either case, the process proceeds back to Step  1731  to either investigate the next pixel location or complete Step  173 . 
         [0059]    Returning to  FIG. 6 , after Step  173  is completed, the motion detection process proceeds to Step  174  to fill in areas between nearby motion pixels. This serves to fill in some of the gaps between areas that were detected as corresponding to motion, improving the accuracy of the motion detection process. Step  174  is described in more detail in the flowchart of  FIG. 9 . 
         [0060]      FIG. 9  depicts a three decision level method which produces the final motion detection map. Beginning with Step  1741 , a square search window within the intermediate motion detection image is created with a radius equal to a lower limit value. Next in Step  1742 , process checks to see if this window has reached its maximum radius. In the event that the window has reached its maximum radius, this signals the completion of Step  174  and the final motion detection map is output. In the event that the window has not reached its maximum radius, the process proceeds to Step  1743  where another check is performed. In Step  1743 , if all the windows of this radius have been processed, the window radius is increased by one pixel length. Otherwise, the process proceeds to Step  1745 , where the four corner pixels that comprise the current search window are selected from the intermediate motion detection map created from Step  173 . Next in Step  1746 , if all four corner pixels are marked as ‘In Motion’ (IM), then all the pixels within the window are marked in the final motion detection map as being ‘In Motion’ (IM). If not all four corner pixels are marked as ‘In Motion’ (IM), then the pixels within the window remain unchanged. In either case, Step  1748  comes next, where the window is moved to the next position to repeat the process. 
         [0061]    Returning to  FIG. 6 , after Step  174  is completed; the motion detection map is also complete, ending the motion detection process. 
         [0062]    Returning to  FIG. 3 , after Step  17  is completed; filtering criteria is applied in Step  18  to the original current image frame to identify areas in advance that are not shadows, improving the accuracy of the process. Step  18  is described in more detail in  FIG. 10 . 
         [0063]      FIG. 10  depicts the two-staged filtering process, starting with Step  181 . In this step the first filtering criteria is applied to the original image, described in more detail in the flowchart of  FIG. 11 . 
         [0064]      FIG. 11  depicts a two decision level method which acts as the first filter. It takes advantage of color information to mark pixel locations that cannot be shadows, allowing these areas that cannot be shadows to be removed from the overall detection process. Beginning at Step  1811 , a check is performed to determine if all pixels in the current image have been processed. In the event that all pixels in the current image have been processed, the first filtering process is complete and the intermediate filter map is output. Otherwise if not all pixels in the current image have been processed, then the process proceeds to Step  1812  where the R, G, B color components are selected for the next pixel Pi. In Step  1813 , the color components are subtracted from each other in the following manner, producing three distinct differences: 
         [0000]      Δ 1   =B−R    (5) 
         [0000]      Δ 2   =R−G    
         [0000]      Δ 3   =G−B    
         [0065]    In Step  1814 , the three differences produced in Step  1813  are compared against thresholds, which are empirically determined. If any or all of the differences fail the comparisons, the current pixel location is marked as ‘Failed’ (  PS ) on the intermediate filter map in Step  1815 . This indicates that the element cannot be a static shadow element and this information can be useful later if this location were erroneously highlighted by the shadow candidates detection of Step  16 . In the alternate case, if all three differences pass the comparisons, the current pixel location is marked as ‘Passed’ (PS) on the intermediate filter map in Step  1816 . After the pixel location is marked, the process next proceeds back to Step  1811  to either repeat the process for another pixel location or complete the process and output the intermediate filter map. 
         [0066]    Returning to  FIG. 10 , after Step  181  is completed, the second filtering criteria are applied in Step  182 , explained in more detail in the flowchart of  FIG. 12 . 
         [0067]      FIG. 12  depicts a four decision level process. Starting with Step  1821  a check is performed to see whether or not all the pixels have been processed accordingly. In the event that all pixels have been processed, Step  182  is complete and the final filter map is output. Otherwise if not all pixels have been processed, the process proceeds to Step  1822  where the next pixel Pi from the current image which is marked in the same location as ‘Passed’ (PS) from the intermediate filter map (Step  181 ) is selected. Next in Step  1823  a check is performed to determine if all the pixels in the neighborhood of P i  have been processed accordingly. In the event that they have not, the process proceeds to Step  1824  where the R, G, B color components are extracted from P i . In Step  1825  the R, G, B color components (in RGB colour space) are transformed into the C 1 C 2 C 3  colour space according to the following equations: 
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         [0068]    Next in Step  1826  the normalized components r, g, b (for the three color basis system) are checked against empirically-determined thresholds. If the normalized components lie within the thresholds, then this indicates that a neighboring element of P i  is considered as a possible shadow element and as such the neighborhood count is incremented by 1 in Step  1827 . If the normalized components do not lie within the thresholds, then the neighboring pixel is not considered as possible a shadow element and the counter is not incremented. In either case the process proceeds back to Step  1822 . When all of the neighboring elements for the selected element P i  have been examined, the process proceeds from Step  1823  to Step  1828 . Here another check is performed to determine if the majority of P i &#39;s neighbors are possible shadow pixels. This is done by simply comparing the neighborhood count against the number of total neighbors. In the event that the majority of neighbors are possible shadow elements the element&#39;s location is marked as ‘Failed’ (  PS ) on the filter map in Step  1829 . Otherwise this is marked as ‘Passed’ (PS) in Step  18210 , indicating the lack of presence of shadow on this location. In either case, the process next proceeds to Step  1821  to either repeat the process for another element P i  or complete the process by having the final binary filter map output. 
         [0069]    Returning to  FIG. 10 , after Step  182  is complete the filtering process is complete and as a result the final filtering map is output. 
         [0070]    Returning to  FIG. 3 , after Step  18  is complete the process proceeds to combine the maps output from Steps  16 ,  17  and  18  to produce the final static shadow map. In Step  19 , the three maps are combined to form the final shadow map. This requires that the shadow candidate map, motion detection map and filter map are available. For each element&#39;s location P i  in the three binary maps, where each map has l total pixels, a logical comparison is performed according to (7). Where the logical comparison (7) is evaluated true the element&#39;s location is marked as ‘Static Shadow’ (SS) in the final binary static shadow map, and where evaluated false marked as ‘Not Static Shadow’ (  SS ). 
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         [0071]    Returning to  FIG. 2 , after static shadow detection in Step  1  is complete, the process next proceeds to glare detection in Step  2 . This process is able to detect the areas in the scene where glare is present, and this step is further detailed in  FIG. 13 . 
         [0072]      FIG. 13  depicts the glare detection process. Here the presence or not of glare as an ambient condition in a scene is detected and mapped accordingly. Beginning with Step  21 , the Graylevel conversion of both the current image (Step  22 ) and previous image (Step  23 ) is carried out. 
         [0073]    In Step  24 , the previous and current graylevel images are compared on a pixel-by-pixel basis, and the maximum pixel difference is attained similar to the process previously outlined in Step  172  on  FIG. 7 . From this an empirically-determined adaptive threshold for glare detection is calculated. 
         [0074]    In Step  25 , the glare pixel candidates are chosen based on how each element&#39;s color information compares to the adaptive threshold calculated prior in Step  24 . On this basis a binary glare candidate map is created. 
         [0075]    In Step  26 , the same motion detection process as has been demonstrated in Step  17  of  FIG. 3  is utilized to determine moving objects by comparing the current and previous images. This creates a binary motion map on which each pixel is marked as either ‘In motion’ (IM) or ‘Not in motion’ (  IM ). 
         [0076]    In Step  27 , the final binary glare map is produced by comparing the glare candidate map of Step  25  with the motion map of Step  26 . Those glare candidates which are marked as IM on the motion map are removed from the final glare map. This allows the glare detection process of the method to exclude moving glare from the detection process, such as ordinary headlights of vehicles which is unnecessary to detect as an ambient condition. 
         [0077]    Returning to  FIG. 2 , after glare detection in Step  2  is complete, the process next proceeds to snow detection in Step  3 . This process detects areas where snow exists in the scene and where recent changes have taken place inside these areas. Examples of situations that would be detected and mapped accordingly include a vehicle driving through snow and leaving a trail, or a maintenance vehicle that pushes snow to the side. This step is further detailed in  FIG. 14 . 
         [0078]      FIG. 14  depicts a snow detection process. Here the presence or not of snow as an ambient condition is detected in the scene and mapped accordingly. Beginning with Step  31 , the background is modeled, using the current image input from Step  32 . This step is further detailed in  FIG. 15 . 
         [0079]      FIG. 15  depicts a single decision level process. This is similar in idea to the background modeling in Step  13  in  FIG. 3 , but has some significant differences. First of all, this process relies on the glare detection process described in  FIG. 13 , it also is only a single level median filter, as opposed to  FIG. 3 , which depicts a dual level median filter whereby the median of the median image is calculated. 
         [0080]    Beginning in Step  311 , the process checks if the median queue is full. In the event that the median queue is not yet full, this would indicate the process must add more frames to the queue to generate a background, and proceeds to Step  312 . In this step the glare detection process is called upon to detect areas affected by glare in the image. This step calls upon the same glare detection process of Step  2  of  FIG. 2 . Next in Step  313  the elements in the neighborhood or close proximity of the glare pixels, detected in Step  312  which generally detects only the umbra of glare, are detected and filled to generate a proximity glare mask and remove proximity glare pixels from the current image. The process of Step  313  essentially enlarges the glare detected areas, filling in the glare penumbras around the detected glare elements from Step  312 . The binary proximity glare map produced in Step  313  is output at this stage to Step  37  of  FIG. 14  for later processing. Each pixel in this map is marked either as ‘Glare’ (GL) or ‘No glare’ (  GL ). 
         [0081]    Next in Step  314  the areas of the current image which are marked as glare are replaced with the values from the last background image generated by Step  31 . Replacing glare elements with previous values allows the background modeling to produce higher quality backgrounds, since the appearance of glare in a scene can otherwise decrease the accuracy of this process. 
         [0082]    Next in Step  316 , the next frame is obtained from the video stream. Background modeling requires a number of frames equal to the length of the median queue before a background image can be generated. Having obtained the next image, the process resumes in Step  311 . 
         [0083]    In Step  311 , in the event that the median queue is full, the process can next generate a background image. In Step  317 , the process generates a median image from the median queue, applying the same steps as in Step  134  and equation (1). 
         [0084]    Next in Step  318 , the queue is cleared and the background image is output to Step  34 , concluding Step  31 . 
         [0085]    Returning to  FIG. 14 , after background modeling in Step  31  is completed; background differencing is next processed in Step  33 . This step is further detailed in  FIG. 16 . 
         [0086]      FIG. 16  depicts a single decision level process. The purpose of background differencing is to produce a map depicting what areas in the image stream have changed since the last background was generated. Beginning in Step  331 , the previous background as generated by Step  31  of  FIG. 14  is introduced to the process. In Step  332 , this previous background is subtracted from the current background and the absolute value of this subtraction is calculated, generated by Step  31  of  FIG. 14 . This step produces a background difference image. 
         [0087]    In Step  333 , the maximum difference in the background difference image is detected. Next in Step  334 , the process checks to determine if all pixels have been processed in the background difference image. In the event they are not, the process continues to Step  335 , where the next element in the difference image is selected. 
         [0088]    Next in Step  336 , the pixel value at the location selected in Step  335  is divided by the maximum image difference value calculated in Step  333 , hence normalizing the value at this location. From here the process continues to Step  334  again. In the event that all of the elements in the background difference image have been processed, it next proceeds to Step  337  where the current background image is saved as the previous background image, allowing it to be used in Step  331  upon next calling of the background differencing process. Concluding this step, the background differencing process of Step  33  is complete and the normalized background difference map image is output. 
         [0089]    Returning to  FIG. 14 , after the completion of Step  33 , the application of thresholds to the background difference image is performed in Step  34 . Here each individual pixel P i  of the background difference image is compared against a threshold. If the individual pixel of the background difference is greater than the threshold, then the pixel is marked as having undergone recent change (CH). Otherwise if the individual pixel is less than the threshold, then that pixel is marked as not having undergone recent change (  CH ) on the binary background differencing map created in this step. 
         [0090]    In Step  37 , the three dimensional correlation is applied to the current background image from Step  35  and the previous background image from Step  36 . The current and previous background images are processed to locate snow in each image and detect changes in snow. This step locates snow in an image by comparing small areas of the current image of some height and width against sample images of snow of the same height and width taken from a database for this particular time of day. The method of comparison here is essentially two dimensional correlation performed on flattened RGB images. Since standard cross-correlation is computationally expensive to calculate in real-time, a more efficient method such as Fast Normalized Cross Correlation (FNCC) is used to calculate cross-correlation. 
         [0091]    In  FIG. 17  the process begins at Step  373 . In Step  371  a snow sample image is chosen based on the time of day and local sunrise/sunset times. The colour snow sample from Step  371 , is then transformed from a three dimensional data structure to a two dimensional data structure with no loss of data. In Step  372  a background image, from either Step  35  or Step  36 , is introduced to the process. The background image is transformed in Step  373  in a manner identical to the transformation which the snow sample image from Step  371  has undergone. 
         [0092]    The transformations of the snow sample image and background image are necessary to allow the calculation of 2D fast normalized cross correlation in Step  374  between the two images based on their colour information. 
         [0093]    Correlation performed in Step  374  produces an intermediate correlation image that may require additional processing. The requirement is determined by calculating the maximum value in the intermediate correlation image in a manner similar to that shown in Step  172 . If the maximum value is greater than an empirically determined threshold, histogram equalization is performed in Step  375  which adjusts the intermediate correlation image to increase the contrast and refine snow detection. If the maximum value is less than that threshold, this is an indication that the background image from Step  372  does not contain snow and histogram equalization is unnecessary, therefore it is not performed. 
         [0094]    After histogram equalization has been performed, or not, the snow correlation image shown in Step  376  is the output of the process. Returning to  FIG. 14 , the change in the scene due to snow is calculated by finding the absolute value of the difference between the current background snow correlation image and the previous background snow correlation image in Step  38 . 
         [0095]    In Step  39 , thresholds are applied to the correlation difference map produced in Step  38 . Pixels which held values above a certain empirically-determined threshold are marked as ‘Snow changed’ (SC), whereas pixels with values below the threshold are marked as ‘Not snow changed’ (  SC ). 
         [0096]    In Step  310 , a comparison is carried out between the glare proximity map from Step  31 , the background difference map from Step  34 , and the snow changed map from Step  39 . For each element location P i  in the three binary maps, where each map has l total pixels, a logical comparison is performed according to (8), where evaluated true the pixel location is marked as ‘Snow’ (SN) in the final binary snow map, and where evaluated false marked the pixel location is marked as ‘Not snow’ (  SN ). 
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         [0097]    Returning to  FIG. 2 , after snow detection in Step  3  is completed, the process next proceeds to rain detection in Step  4 . This step detects areas where rain or water deposits exist in a scene and where recent changes have taken place inside these areas. Examples of situations that would be detected and mapped accordingly include a vehicle driving through a puddle on a roadway and splashing water or leaving a trail. Moreover, rain detection in Step  4  can also detect rain droplets collecting on a video camera. This process of rain detection is almost identical in process to snow detection, as has been illustrated in Step  3  prior. At the conclusion of this process a rain map is output. 
         [0098]    After Step  4  is complete, the method has finished one complete cycle. This process as a whole may repeat indefinitely as new images of the scene are input, with the method continuing to illuminate the static shadow, glare, snow and rain in a scene. This method can be used in any system for automatic detection of the ambient conditions in a scene from a sequence of images. 
         [0099]    The method for detecting environmental conditions in a sequence of images can be implemented on a computer system. A process of implementing the method may be stored on computer readable material. The computer system can acquire successive frames of data and the data processor can process the frames of data as a part of a method of implementing the AID system. 
         [0100]    In the claims, the word “comprising” is used in its inclusive sense and does not exclude other elements being present. The indefinite article “a” before a claim feature does not exclude more than one of the feature being present. 
         [0101]    Immaterial modifications may be made to the embodiments described here without departing from what is covered by the claims.
A method for determining the presence and location of static shadows and other ambient conditions (such as glare, snow, rain, etc.) in a series of time-successive images is provided. Each image comprises a series of image elements locatable on a plane, with each element being associated with a color defined by three chromatic elements. Furthermore, each image is partitioned into a set of elements, with each element comprising one or more pixels. According to the process of the present method, the ambient conditions are detected using a mixture of processes which utilize the chromatic elements, luminance qualities and temporal characteristics of the series of images.