{"id":"W2014509618","doi":"10.1117/12.734656","title":"&lt;title&gt;Improved multi-target tracking using probability hypothesis density smoothing&lt;/title&gt;","year":2007,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Smoothing; Computer science; Tracking (education); Algorithm; Particle filter; Bayesian probability; Gaussian; Filter (signal processing); Monte Carlo method; Moment (physics); Observable; Nonlinear system; Mathematical optimization; Artificial intelligence; Mathematics; Statistics; Computer vision","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":[0.001098659,0.0004482859,0.0007674192,0.0006610788,0.0002478843,0.0006969755,0.0008182774,0.0009248868,0.007576133],"category_scores_gemma":[0.002547104,0.000236474,0.0005817159,0.0009353127,0.0004147872,0.001172733,0.0005844657,0.0009272559,0.003484517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005165059,"about_ca_system_score_gemma":0.0005973855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002615031,"about_ca_topic_score_gemma":0.002486856,"domain_scores_codex":[0.9994705,0.0001057719,0.0000285374,0.0001207298,0.0002430484,0.00003137041],"domain_scores_gemma":[0.9988405,0.0003944486,0.00007553677,0.0002822914,0.0003688224,0.00003851996],"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":[0.0004349492,0.0001157167,0.001342536,0.0002161881,0.00009692541,0.0002636677,0.00007317696,0.2607026,0.04778155,0.02291183,0.01875584,0.6473051],"study_design_scores_gemma":[0.0000135846,0.00003503972,0.0003528974,0.000006544773,0.000009174444,0.00006526308,0.00000282483,0.9819142,0.01028317,0.00228017,0.005018753,0.0000182129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004092284,0.0002521234,0.9918455,0.0001949458,0.0001966586,0.00002667329,0.00006492285,0.001101772,0.002225098],"genre_scores_gemma":[0.2273285,0.0006707341,0.7519626,0.000305487,0.0003081482,0.0001001798,0.0006942261,0.0004415282,0.01818863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007576133,"threshold_uncertainty_score":0.02534473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02440588831645742,"score_gpt":0.237927079623099,"score_spread":0.2135211913066416,"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."}}