{"id":"W2074220689","doi":"10.1109/crv.2010.52","title":"Max-Margin Offline Pedestrian Tracking with Multiple Cues","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Polytechnique Montréal; Simon Fraser University","funders":"","keywords":"Robustness (evolution); BitTorrent tracker; Computer science; Artificial intelligence; Discriminative model; Margin (machine learning); Pedestrian; Tracking (education); Computer vision; Pedestrian detection; Data association; Machine learning; Pattern recognition (psychology); Eye tracking; Engineering","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":[0.001459874,0.001168944,0.001810713,0.001038964,0.0005690978,0.001222103,0.001815752,0.001182424,0.002052464],"category_scores_gemma":[0.00333,0.0007774552,0.0007446769,0.001256662,0.0004854015,0.002365788,0.001907098,0.001219557,0.001306913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006603012,"about_ca_system_score_gemma":0.0009526305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001680701,"about_ca_topic_score_gemma":0.002824201,"domain_scores_codex":[0.999001,0.0001994867,0.00004393719,0.0003973827,0.0002551821,0.0001030383],"domain_scores_gemma":[0.9989191,0.0003345163,0.0001555408,0.0002940441,0.0002105812,0.00008626442],"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":[0.0009744206,0.0003031033,0.003848828,0.0001621357,0.0001122548,0.0001435273,0.0001956446,0.1473589,0.03570677,0.0070672,0.003850427,0.8002768],"study_design_scores_gemma":[0.00002058331,0.0001224014,0.001232238,0.00001538692,0.00002689059,0.0001361668,0.00002665157,0.9721834,0.01958587,0.003997809,0.002629273,0.0000233742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01006443,0.0001426728,0.9881207,0.00003453785,0.00002552611,0.00001979319,0.00003610534,0.0009300738,0.0006261417],"genre_scores_gemma":[0.3536784,0.0001899155,0.6408753,0.0001507246,0.00008020749,0.00008459327,0.0005054183,0.0003171726,0.004118153],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002052464,"threshold_uncertainty_score":0.00772059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02233360162132039,"score_gpt":0.2769917280985035,"score_spread":0.2546581264771831,"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."}}