{"id":"W2014959166","doi":"10.1145/1992896.1992914","title":"Occlusion handling based on sub-blobbing in automated video surveillance system","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Tracking (education); Context (archaeology); Occlusion; Video tracking; Feature (linguistics); Process (computing); Object detection; Object (grammar); Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007207003,0.0002894996,0.0006832305,0.0007923171,0.0005024739,0.0005065468,0.0004666309,0.0004601969,0.0005618155],"category_scores_gemma":[0.001604599,0.0002602093,0.0003051074,0.0006698568,0.0004056818,0.0006743782,0.0004222804,0.0003066048,0.0002170072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005453525,"about_ca_system_score_gemma":0.0004556576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006038995,"about_ca_topic_score_gemma":0.004104295,"domain_scores_codex":[0.9993877,0.0001605931,0.00002702548,0.0001213288,0.0002309914,0.0000722346],"domain_scores_gemma":[0.9993599,0.0001933358,0.0001372617,0.0000927951,0.0001681562,0.0000485376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001021945,0.0002092287,0.00723228,0.0001500554,0.0001055345,0.0005615422,0.0008018041,0.1361836,0.1979185,0.005749411,0.002005717,0.6480604],"study_design_scores_gemma":[0.00002130203,0.0001277628,0.007524418,0.00001025905,0.00004603758,0.0002558759,0.00003663759,0.9629029,0.02546355,0.001489297,0.002096046,0.00002589102],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.189328,0.0005657904,0.8067871,0.00007509718,0.00003150178,0.00005468309,0.00002961607,0.001260147,0.001868061],"genre_scores_gemma":[0.81192,0.0003483328,0.1859312,0.00004041245,0.00003362246,0.00003467311,0.00008510146,0.00006300388,0.001543612],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006038995,"threshold_uncertainty_score":0.01200771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03080439445709003,"score_gpt":0.2621430520961431,"score_spread":0.231338657639053,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}