{"id":"W4289709938","doi":"10.48550/arxiv.1808.01066","title":"Online Illumination Invariant Moving Object Detection by Generative\\n Neural Network","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Invariant (physics); Benchmark (surveying); Computer vision; Representation (politics); Batch processing; Generative model; Pattern recognition (psychology); Image (mathematics); 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":[0.0003782556,0.001016058,0.0009809484,0.0006424997,0.0003255075,0.0006022017,0.001875706,0.0009409645,0.001462412],"category_scores_gemma":[0.001127195,0.0005791053,0.0007585782,0.0006204004,0.0005329682,0.0008954685,0.0008125462,0.0013217,0.0005196369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009480832,"about_ca_system_score_gemma":0.000869618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01276202,"about_ca_topic_score_gemma":0.01697946,"domain_scores_codex":[0.9997051,0.00002711581,0.00001018353,0.0001304294,0.00007415671,0.00005301899],"domain_scores_gemma":[0.9996132,0.0001537939,0.00005885474,0.00006310098,0.00008264661,0.00002832404],"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":[0.0003298759,0.0002059296,0.001553796,0.00009540947,0.0001437075,0.0001451809,0.00005588741,0.3105016,0.02267371,0.002522636,0.004993557,0.6567788],"study_design_scores_gemma":[0.00000436235,0.00001270689,0.0002271248,0.000001943993,0.000007877988,0.00001721285,0.000002135397,0.9966336,0.002324163,0.0005317773,0.0002333764,0.000003815894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06418861,0.001262842,0.9258075,0.0002763689,0.0001527495,0.00007362197,0.0001590365,0.005049051,0.00303036],"genre_scores_gemma":[0.6660309,0.0006157267,0.3220947,0.0004690813,0.0001416119,0.0001001468,0.0009999779,0.0003657838,0.009182139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01276202,"threshold_uncertainty_score":0.02537543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06756382013493388,"score_gpt":0.2201724781855581,"score_spread":0.1526086580506242,"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."}}