{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008860203,0.0001103158,0.0001403351,0.00007429003,0.0001169184,0.0001499592,0.0003730451,0.00005350484,0.00001376683],"category_scores_gemma":[0.0001080904,0.00009653198,0.00006609858,0.0001925037,0.00001987412,0.0004604419,0.00009520598,0.0001016936,0.00002809812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003122385,"about_ca_system_score_gemma":0.00001534759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002344294,"about_ca_topic_score_gemma":0.0001105273,"domain_scores_codex":[0.998826,0.0002165483,0.0001775535,0.0003229237,0.0001947986,0.0002622046],"domain_scores_gemma":[0.9991776,0.0002315924,0.00005682467,0.0004160292,0.00005679848,0.00006116583],"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":[0.000001621791,0.00002883853,0.1029135,0.00001269068,0.0000069334,0.000004033542,0.000176342,0.003893412,0.00165637,0.003555459,0.00001114526,0.8877396],"study_design_scores_gemma":[0.0002734053,0.00003019519,0.1017904,0.00001996768,0.000002446041,0.00002072714,0.00001795799,0.8784668,0.01749847,0.001347866,0.0003561158,0.0001757019],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1072576,0.000009211964,0.8911591,0.00008412629,0.0004021033,0.00005106264,3.688805e-7,0.0002302731,0.0008061451],"genre_scores_gemma":[0.845637,0.000001258963,0.1539843,0.0002443364,0.0001011799,0.000001480835,9.329513e-7,0.000007894665,0.00002159662],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8875639,"threshold_uncertainty_score":0.393646,"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."}}