{"id":"W1985145931","doi":"10.5244/c.28.92","title":"Multiple Object Tracking Using Local Motion Patterns","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Object (grammar); Markov chain; Motion (physics); Video tracking; Tracking (education); Set (abstract data type); Data association; Association (psychology); Pattern recognition (psychology); Algorithm; Machine learning","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":[0.00134492,0.0007605992,0.001208435,0.002136613,0.0007814234,0.001203927,0.001974309,0.001198633,0.001412576],"category_scores_gemma":[0.004168981,0.0007682102,0.001099528,0.002273413,0.000576401,0.001905432,0.001929883,0.001525202,0.001075436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000921223,"about_ca_system_score_gemma":0.001065188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003794753,"about_ca_topic_score_gemma":0.004627466,"domain_scores_codex":[0.9989183,0.0001134843,0.00005599555,0.0004020978,0.0004395789,0.00007067667],"domain_scores_gemma":[0.9987356,0.0005051313,0.0001749483,0.0002833529,0.0002406035,0.00006043788],"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":[0.000194913,0.000139487,0.00342166,0.0001307885,0.0001385587,0.0001577171,0.0002294808,0.1940583,0.01923579,0.01529704,0.002396036,0.7646002],"study_design_scores_gemma":[0.0000198761,0.00004688233,0.001064638,0.00002120266,0.00002654397,0.0001436072,0.00001855324,0.9752184,0.008523691,0.0101124,0.004781806,0.00002237812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002233955,0.00006073054,0.9969935,0.00002242387,0.00001254358,0.00002251511,0.00003079641,0.0003590626,0.0002644732],"genre_scores_gemma":[0.07778227,0.0001729822,0.9195784,0.00006304066,0.00003452413,0.0001581164,0.0003608778,0.0001306889,0.001719092],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003794753,"threshold_uncertainty_score":0.007545352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04469162574395924,"score_gpt":0.2971600427281662,"score_spread":0.252468416984207,"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."}}