The National Marine Fisheries Service (NMFS) of the United States National Oceanic and Atmospheric Administration (NOAA), the Department of Fisheries of Western Australia, the Australia Institute of Marine Science, the European Union and many NGOs, private conservation groups, and large-scale aquaculture operations are increasingly using optical data streams to augment traditional terrestrial, marine, and aquatic wildlife surveys. A report by the US National Task Force for Improving Stock Assessment found the greatest impediment to producing accurate, precise, and credible stock assessments was the lack of adequate input data. If properly employed, camera-based surveys can reduce sampling error, increase sampling intensity and increase the spatio-temporal area or number of species surveyed. However, data volume quickly exceeds human processing capabilities. To date, this has been mitigated by subsampling available data, by using analysis methods that may not accurately characterize wildlife assemblages, and initial efforts at automating image and video analysis. Automated wildlife classifiers —similar to those that have been developed for human surveillance and biomedical applications—must be developed to effectively make use of available data. To this end, NOAA Fisheries has initiated a Strategic Initiative on Automated Image Analysis. Operating underwater; classifying cryptic, camouflaged, or morphologically similar individuals generates significant challenges not unlike those faced by aerial human surveillance. Lately, there has been a significant increase in vision algorithm development for wildlife video of animals including birds, bats, bees, flies, fish, large terrestrial mammals, and so on.
2017-03-30 United States Santa Rosa,USAThe 3nd Workshop on Automated Analysis of Video Data for Wildlife Surveillance
Shirika la Uvuvi wa Bahari la Marekani (NOAA), Shirika la Uvuvi la Australia Magharibi, Taasisi ya Australia ya Sayansi ya Bahari, Umoja wa Ulaya na mashirika mengi ya kiserikali, makundi ya uhifadhi wa kibinafsi, na shughuli kubwa za ufugaji wa samaki zinatumia zaidi na zaidi mikondo ya data ya macho ili kuongeza uchunguzi wa jadi wa wanyama wa baharini, baharini na majini. Ripoti ya Timu ya Kazi ya Kitaifa ya Marekani ya Kuboresha Tathmini ya Hifadhi iligundua kizuizi kikubwa cha kutengeneza tathmini sahihi, sahihi, na ya kuaminika ya hifadhi ni ukosefu wa data ya kutosha ya pembejeo. Kama kutumika vizuri, kamera-msingi uchunguzi inaweza kupunguza sampuli makosa, kuongeza sampuli nguvu na kuongeza eneo spatio-wakati au idadi ya spishi kuchunguzwa. Hata hivyo, haraka kiasi cha data kinazidi uwezo wa binadamu wa kuchanganua. Hadi sasa, hii imepunguzwa kwa subsampling data inapatikana, kwa kutumia mbinu za uchambuzi ambayo inaweza si sahihi sifa ya wanyama pori makundi, na juhudi za awali katika automatisering picha na video uchambuzi. Automatic wanyama pori classifiers <unk> sawa na wale ambao wamekuwa maendeleo kwa ajili ya ufuatiliaji wa binadamu na maombi ya biomedical <unk> lazima kuendelezwa kwa ufanisi kutumia data inapatikana. Kwa kusudi hili, NOAA Uvuvi imeanzisha Mpango wa Mkakati juu ya Uchambuzi wa Picha ya Kiotomatiki. Kufanya kazi chini ya maji; kuainisha watu wasioeleweka, waliofichwa, au waliofanana kimaumbile hutokeza changamoto kubwa ambazo hazitofautiani na zile zinazokabiliwa na ufuatiliaji wa kibinadamu wa angani. Hivi karibuni, kumekuwa na ongezeko kubwa katika maendeleo ya algorithm ya maono kwa video ya wanyama wa porini, pamoja na ndege, popo, nyuki, nzi, samaki, mamalia wakubwa wa nchi kavu, na kadhalika. 2017-03-30 Santa Rosa, USA - Mkutano wa tatu wa Uchambuzi wa Kiotomatiki wa Takwimu za Video kwa Ufuatiliaji wa Wanyamapori