{"id":"W206307230","doi":"10.1007/978-3-642-23866-6_12","title":"Detecting Separation of Moving Objects Based on Non-parametric Bayesian Scheme for Tracking by Particle Filter","year":2011,"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":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Particle filter; Computer vision; Computer science; Tracking (education); Object (grammar); Artificial intelligence; Parametric statistics; Video tracking; Separation (statistics); Object detection; Scheme (mathematics); Filter (signal processing); Bayesian probability; Group (periodic table); Process (computing); Pattern recognition (psychology); Mathematics; Machine learning; Physics","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":[0.00112785,0.0007385834,0.001460567,0.0009036555,0.0004457981,0.001143183,0.001496802,0.001686707,0.0009356826],"category_scores_gemma":[0.002613232,0.0007356878,0.001111019,0.001307572,0.0005761401,0.001906867,0.001396418,0.001511683,0.0005333076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004720299,"about_ca_system_score_gemma":0.0008264343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002143563,"about_ca_topic_score_gemma":0.001984085,"domain_scores_codex":[0.9992343,0.0001311362,0.00005153761,0.0002118758,0.00031432,0.00005673551],"domain_scores_gemma":[0.9993099,0.0003206558,0.00007542984,0.0001134848,0.0001571198,0.00002338313],"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":[0.0003820835,0.0001547545,0.001580946,0.0003703587,0.00020262,0.0001685077,0.0002094679,0.1854496,0.06902094,0.02283946,0.002809048,0.7168123],"study_design_scores_gemma":[0.00001346271,0.00004546751,0.0006387498,0.00001023461,0.00003448486,0.0001349469,0.000009889824,0.9835185,0.008301665,0.006047965,0.001220425,0.00002424855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002740713,0.0001703688,0.9966595,0.00001889248,0.0000209771,0.00001154897,0.000009587425,0.0001322421,0.0002361151],"genre_scores_gemma":[0.1457259,0.0007893529,0.8501314,0.00006282899,0.00006034228,0.00007752257,0.0002253439,0.00007117658,0.002856094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002143563,"threshold_uncertainty_score":0.005964756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02433356330101103,"score_gpt":0.2610276205054576,"score_spread":0.2366940572044466,"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."}}