{"id":"W2885185277","doi":"10.1145/3293353.3293369","title":"Online Illumination Invariant Moving Object Detection by Generative Neural Network","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Invariant (physics); Benchmark (surveying); Batch processing; Computer vision; Representation (politics); 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001318838,0.0003353904,0.0003635079,0.0001160921,0.0002533777,0.0004382733,0.0008872655,0.0003111501,0.00002189248],"category_scores_gemma":[0.00017379,0.0003045569,0.0001361895,0.0003806949,0.00006173112,0.0003589067,0.001164341,0.0005925222,0.00001784727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000124631,"about_ca_system_score_gemma":0.0001010818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000317553,"about_ca_topic_score_gemma":0.0006688504,"domain_scores_codex":[0.997218,0.0006687887,0.0004254863,0.0009290136,0.000353145,0.0004055542],"domain_scores_gemma":[0.9982486,0.0002116603,0.0003324732,0.0008409413,0.0002790719,0.00008723076],"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":[0.00003036158,0.0003068776,0.001501701,0.0001243069,0.0002594023,0.00004114583,0.00163751,0.0612461,0.006531577,0.002332772,0.01042201,0.9155663],"study_design_scores_gemma":[0.0001863108,0.000128717,0.006031008,0.00005095955,0.00001385107,0.00001735222,0.00001467109,0.9742174,0.009213435,0.008782031,0.0008350056,0.0005093274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03568855,0.0002491589,0.9583997,0.000513047,0.003585382,0.0002910811,0.00001229084,0.0004265785,0.0008342866],"genre_scores_gemma":[0.6552591,0.00005123052,0.3417295,0.0007632167,0.001727416,0.00003471372,0.00008972228,0.00002584482,0.0003192275],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9150569,"threshold_uncertainty_score":0.9999406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0330031697146688,"score_gpt":0.2983407908381686,"score_spread":0.2653376211234997,"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."}}