{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005422817,0.00009711825,0.0001052519,0.00004211918,0.0003761404,0.0001385751,0.0004436253,0.00002326762,0.0001473631],"category_scores_gemma":[0.000005701235,0.00009331814,0.0000512517,0.0003694184,0.00001637462,0.0003909048,0.0002136786,0.0002504938,0.000007756912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008149141,"about_ca_system_score_gemma":0.0000182269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005687978,"about_ca_topic_score_gemma":0.00003181324,"domain_scores_codex":[0.9987506,0.0003418,0.000140159,0.0003209234,0.0002161959,0.000230341],"domain_scores_gemma":[0.9994349,0.0001248589,0.00006105401,0.0003067263,0.00002600399,0.00004648706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004063703,0.0003472423,0.004831076,0.000007595452,0.00009453118,0.0000830303,0.0006032616,0.3817617,0.006484916,0.03254204,0.04575297,0.527451],"study_design_scores_gemma":[0.000140867,0.00007924286,0.002712893,3.16326e-7,0.000001571005,0.00003662619,0.0000455752,0.9898847,0.0001129364,0.0006835312,0.006165958,0.0001357665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03646044,0.0002388546,0.9594732,0.001064993,0.001138221,0.00007610778,0.000002238533,0.0001853937,0.001360541],"genre_scores_gemma":[0.9614069,0.00001144181,0.03596745,0.001222354,0.00003752037,0.00002881932,0.00001693591,0.000008257478,0.001300247],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9249465,"threshold_uncertainty_score":0.3805403,"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."}}