{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007848492,0.000349011,0.0003529582,0.0001855073,0.0003495958,0.0002125289,0.001198229,0.0003274185,0.00001426562],"category_scores_gemma":[0.00009711647,0.0003984853,0.0001800411,0.0007864591,0.000113982,0.0005411305,0.001221547,0.0006150357,0.00002424247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002303663,"about_ca_system_score_gemma":0.0001103822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002393681,"about_ca_topic_score_gemma":0.0004115913,"domain_scores_codex":[0.9972838,0.0006834393,0.0002568387,0.001208233,0.0001290751,0.0004385873],"domain_scores_gemma":[0.9980873,0.0001812984,0.0003735543,0.0009701918,0.0002611541,0.0001265011],"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":[0.00007894896,0.00033841,0.00720096,0.0001184847,0.0002999691,0.0003646959,0.0006850421,0.9274218,0.002013364,0.0118661,0.001850271,0.04776194],"study_design_scores_gemma":[0.0003036818,0.0001038198,0.005462142,0.00004964305,0.00003466164,0.000008843656,0.00002131398,0.9785413,0.001188799,0.01339474,0.000408665,0.000482424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2927933,0.00008084071,0.7049617,0.00007217383,0.00138033,0.0001772396,0.0000138931,0.0002488118,0.0002717424],"genre_scores_gemma":[0.9825621,0.00009729419,0.01606813,0.0001975212,0.0006134107,0.000001212407,0.0000559389,0.00002310417,0.000381351],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6897687,"threshold_uncertainty_score":0.9998467,"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."}}