{"id":"W2110671801","doi":"","title":"Incremental Learning for Visual Tracking","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":288,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Particle filter; Artificial intelligence; Computer science; Eye tracking; Tracking (education); Video tracking; Computer vision; Subspace topology; Representation (politics); Inference; Active appearance model; Markov chain Monte Carlo; Task (project management); Pattern recognition (psychology); Object (grammar); Kalman filter; Bayesian probability; 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":[0.001072327,0.0009016181,0.001166737,0.0009652076,0.0005379992,0.0008620996,0.002420878,0.0009025154,0.002800075],"category_scores_gemma":[0.006180617,0.0006090382,0.0007437599,0.001010794,0.0007658259,0.001691367,0.001467809,0.001477162,0.0009950323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008492221,"about_ca_system_score_gemma":0.0008623444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005654857,"about_ca_topic_score_gemma":0.005119538,"domain_scores_codex":[0.9993561,0.0001459239,0.00002825751,0.0001796094,0.0002253452,0.00006476196],"domain_scores_gemma":[0.997931,0.001163441,0.000151916,0.0003259366,0.0003565807,0.00007117567],"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":[0.0001650838,0.0001370323,0.0010859,0.0001781099,0.00007919109,0.0001122247,0.0001055845,0.4313043,0.004705615,0.02962303,0.00534404,0.5271598],"study_design_scores_gemma":[0.000009550943,0.00002466852,0.0001215328,0.000005893634,0.000009263521,0.0000293714,0.000004224509,0.9851668,0.001123688,0.01229865,0.001198887,0.000007363945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003357198,0.0002563664,0.9943765,0.00005868477,0.00003778642,0.00003364841,0.00004091247,0.0009281322,0.0009108365],"genre_scores_gemma":[0.4326433,0.0008879277,0.5606862,0.0002351909,0.0001977986,0.0004481108,0.0005730758,0.0002523536,0.004076093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005654857,"threshold_uncertainty_score":0.01124394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0371558157009693,"score_gpt":0.3422921101543846,"score_spread":0.3051362944534153,"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."}}