{"id":"W2147953023","doi":"10.1007/978-3-540-24670-1_3","title":"A Boosted Particle Filter: Multitarget Detection and Tracking","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":1029,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"AdaBoost; Particle filter; Artificial intelligence; Tracking (education); Computer science; Computer vision; Filter (signal processing); Mixture model; Video tracking; Gaussian; Context (archaeology); Pattern recognition (psychology); Object detection; Object (grammar); Classifier (UML)","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":[0.0009959921,0.0009645462,0.001262349,0.000737978,0.0003834129,0.001038252,0.001659281,0.002330281,0.004540222],"category_scores_gemma":[0.002317689,0.0009248252,0.0007654772,0.001705568,0.0004851038,0.001620726,0.001155988,0.001994339,0.004431722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004577412,"about_ca_system_score_gemma":0.0006065857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002786384,"about_ca_topic_score_gemma":0.002851936,"domain_scores_codex":[0.9993594,0.0001151219,0.00002976954,0.0001504022,0.0003149961,0.00003030697],"domain_scores_gemma":[0.9993486,0.0002451505,0.00002567002,0.0001285932,0.0002219076,0.00003006609],"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":[0.0001805974,0.00008952418,0.0002475775,0.0002848349,0.0001212455,0.0001075319,0.00005598013,0.1180748,0.0181805,0.01801698,0.01677023,0.8278703],"study_design_scores_gemma":[0.0000205642,0.00004187238,0.0002278542,0.00001272569,0.00003543895,0.000114606,0.00000531016,0.9715014,0.006667161,0.00847392,0.01287812,0.00002113053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004777772,0.0009291917,0.9970675,0.00004991062,0.0001759971,0.00001277417,0.00002140316,0.000551958,0.00071351],"genre_scores_gemma":[0.03538112,0.002134716,0.9502252,0.0001793664,0.0003091478,0.0001027834,0.0002178184,0.0002067018,0.01124318],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004540222,"threshold_uncertainty_score":0.01518857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01866135330152782,"score_gpt":0.2339072648257161,"score_spread":0.2152459115241882,"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."}}