{"id":"W4309918511","doi":"10.1109/avss56176.2022.9959543","title":"Dynamic Background Subtraction by Generative Neural Networks","year":2022,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Background subtraction; Computer science; Artificial intelligence; Artificial neural network; Generative model; Subtraction; Frame (networking); Code (set theory); Computer vision; Entropy (arrow of time); Pattern recognition (psychology); Foreground detection; Pixel; Generative grammar; Mathematics","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":[0.0005765455,0.001156568,0.000779988,0.0008114699,0.0003585636,0.0008202325,0.001494933,0.000889298,0.002086825],"category_scores_gemma":[0.001386544,0.0006862401,0.001145779,0.0007425832,0.000636204,0.0007504298,0.001095493,0.001514605,0.0009575853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009191114,"about_ca_system_score_gemma":0.0006956016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006595274,"about_ca_topic_score_gemma":0.0110333,"domain_scores_codex":[0.9996058,0.00007858934,0.00001181594,0.0001416945,0.0001117958,0.00005034508],"domain_scores_gemma":[0.9995762,0.0001995718,0.00004773212,0.00006428684,0.00008843008,0.00002377069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001134293,0.00006494491,0.0008014156,0.00007649302,0.0001289654,0.0001170837,0.00007663015,0.666205,0.01846364,0.009171743,0.002736798,0.3020439],"study_design_scores_gemma":[0.00000309788,0.000006611615,0.0001257937,0.000004053844,0.000007449301,0.00002643501,0.000003171988,0.9941075,0.002759673,0.002384265,0.0005668765,0.000005050554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008007674,0.00019309,0.988921,0.000081099,0.00003099019,0.00002261844,0.00005365411,0.001406605,0.001283228],"genre_scores_gemma":[0.3756465,0.0004841442,0.6121152,0.0004842929,0.0001058881,0.0001224249,0.001012237,0.0009885855,0.009040731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006595274,"threshold_uncertainty_score":0.0131138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02342458219514736,"score_gpt":0.2932536192686339,"score_spread":0.2698290370734865,"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."}}