{"id":"W1966542767","doi":"10.1117/12.488522","title":"<title>Hybrid weighted interacting particle filter for multitarget tracking</title>","year":2003,"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":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Particle filter; Resampling; Auxiliary particle filter; Computer science; Filter (signal processing); Tracking (education); SIGNAL (programming language); Algorithm; Nonlinear system; Nonlinear filter; Artificial intelligence; Control theory (sociology); Filter design; Computer vision; Physics; Kalman filter; Ensemble Kalman filter; Extended Kalman filter","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004125877,0.0001864161,0.0002172199,0.00005027838,0.00008211139,0.0001471728,0.0007288831,0.00009511749,0.00008531693],"category_scores_gemma":[0.0005609385,0.0001574714,0.0003041857,0.0002079454,0.00006768518,0.0004284534,0.0001010642,0.0001914229,0.00002077084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005724169,"about_ca_system_score_gemma":0.000022286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.535607e-7,"about_ca_topic_score_gemma":4.291022e-8,"domain_scores_codex":[0.9986253,3.429364e-8,0.0003986965,0.0003154406,0.0003448784,0.0003156423],"domain_scores_gemma":[0.9986336,0.0001952032,0.0001912667,0.00007750528,0.0008169738,0.00008540029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001771644,0.00008336118,0.00006080879,0.0001173509,0.0001275989,1.898635e-7,0.0001044537,0.00006047338,0.09910826,0.8300924,0.06804793,0.00217941],"study_design_scores_gemma":[0.001096992,0.0002036541,0.0001425303,0.0003179641,0.00008533928,0.00003697725,0.000150229,0.3955789,0.1799675,0.004908566,0.416942,0.000569323],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9705174,0.0002164108,0.006980429,0.001165727,0.001409704,0.000439788,0.00005847697,0.0002051673,0.01900691],"genre_scores_gemma":[0.4845018,0.00007233526,0.5131406,0.0001878473,0.0005771811,0.00008816268,0.00001571192,0.00006356836,0.001352809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8251839,"threshold_uncertainty_score":0.6421498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01506808229801794,"score_gpt":0.2341321115374052,"score_spread":0.2190640292393873,"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."}}