{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00124108,0.0001715996,0.0002013474,0.0001036913,0.0001105555,0.000187566,0.000517685,0.00005808911,0.000003354595],"category_scores_gemma":[0.0002975758,0.0001438517,0.0000749459,0.0003489735,0.00001971911,0.0004154804,0.00006265056,0.0001658078,0.00008083804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004998377,"about_ca_system_score_gemma":0.0001332813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000748551,"about_ca_topic_score_gemma":0.00005555135,"domain_scores_codex":[0.99835,0.0002496512,0.0002125619,0.0004813774,0.0003816517,0.0003248066],"domain_scores_gemma":[0.9988983,0.0001431089,0.00005492616,0.00059325,0.0001651598,0.0001452615],"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":[0.00002526369,0.0000743575,0.002877624,0.000005724946,0.000008219575,0.00002254367,0.0005370733,0.9496698,0.0004093657,0.003066186,0.0001479248,0.04315587],"study_design_scores_gemma":[0.0006505759,0.00008379111,0.0003501894,0.00003014104,0.000001431787,0.000003099486,0.0000267394,0.9912282,0.005657084,0.001537225,0.0002273751,0.0002040984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01896307,0.00009059464,0.9737671,0.0004975883,0.0004398972,0.0001247423,8.338347e-7,0.0003150506,0.0058011],"genre_scores_gemma":[0.7081318,0.00000163752,0.2907668,0.0009335256,0.00008996057,0.000009159133,9.523616e-7,0.000009615812,0.0000564966],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6891688,"threshold_uncertainty_score":0.5866103,"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."}}