{"id":"W2294411327","doi":"10.1109/icip.2015.7351560","title":"Multiple object tracking based on sparse generative appearance modeling","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Active appearance model; Computer vision; Object (grammar); Video tracking; Focus (optics); Generative model; Tracking (education); Similarity (geometry); Feature (linguistics); Pattern recognition (psychology); Generative grammar; Image (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":[0.001020707,0.0007280112,0.001327697,0.001448719,0.0003765659,0.000964548,0.001788619,0.0009434947,0.0009159148],"category_scores_gemma":[0.002747606,0.000640682,0.001438733,0.001660446,0.0005640161,0.001567652,0.001188387,0.001498601,0.0007168842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007071792,"about_ca_system_score_gemma":0.0005821019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004932283,"about_ca_topic_score_gemma":0.004795747,"domain_scores_codex":[0.9992371,0.0001243087,0.00002682719,0.0002337332,0.0003112692,0.00006689494],"domain_scores_gemma":[0.998836,0.0004860272,0.0001667231,0.0002299605,0.0002181953,0.00006321967],"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":[0.0001535228,0.0001149483,0.002436979,0.0001100739,0.0001596291,0.0002565823,0.0001878237,0.5220633,0.02753556,0.01341844,0.002376977,0.4311861],"study_design_scores_gemma":[0.000003014151,0.00001167544,0.0001611894,0.000002868897,0.000008543399,0.00005592917,0.00000355058,0.9962063,0.001626963,0.001567776,0.0003467002,0.0000054291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005507934,0.0001095938,0.9934338,0.00003935776,0.00002000849,0.00001267007,0.00001979359,0.000480771,0.0003761133],"genre_scores_gemma":[0.4532197,0.0005209809,0.5417379,0.0001891469,0.000100653,0.00008470113,0.0004717635,0.000295905,0.003379181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004932283,"threshold_uncertainty_score":0.009807169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1267869058218597,"score_gpt":0.3195410560460898,"score_spread":0.1927541502242301,"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."}}