{"id":"W2057357148","doi":"10.1117/12.896465","title":"Improved multiframe association for tracking maneuvering targets","year":2011,"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":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; McMaster University","funders":"","keywords":"Computer science; Data association; Association (psychology); False alarm; Tracking (education); Artificial intelligence; Computer vision; Constant false alarm rate; Recursion (computer science); Algorithm; Filter (signal processing)","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":[0.001663623,0.000769069,0.0009486497,0.0009717548,0.0006197858,0.0005613024,0.001202188,0.001108026,0.001098094],"category_scores_gemma":[0.003482812,0.0004161998,0.0007614481,0.001424706,0.0004167403,0.002041701,0.001543263,0.00141755,0.0005807717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005755466,"about_ca_system_score_gemma":0.0008884853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00273504,"about_ca_topic_score_gemma":0.003640272,"domain_scores_codex":[0.9989785,0.0002335125,0.00005328717,0.0002277107,0.0004092771,0.00009773532],"domain_scores_gemma":[0.9987131,0.0005284035,0.0001632449,0.0002464238,0.0002990593,0.00004969627],"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":[0.0004227974,0.0002383722,0.002429113,0.0001236502,0.00007824817,0.0001445675,0.0002953038,0.3001627,0.05193185,0.01280237,0.001538787,0.6298322],"study_design_scores_gemma":[0.000008260939,0.00004292954,0.0003545996,0.000003344537,0.000007082837,0.00007852876,0.000007591141,0.9912511,0.00549123,0.001714517,0.001026679,0.00001410885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.011131,0.0002070051,0.9879765,0.00005755943,0.00003066346,0.00001186836,0.00002161315,0.0002793406,0.0002843968],"genre_scores_gemma":[0.2157241,0.0001858921,0.7821677,0.00008322994,0.00004437541,0.00004556142,0.0001758863,0.00007050495,0.001502828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00273504,"threshold_uncertainty_score":0.008798182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01754640900970787,"score_gpt":0.2251870694929826,"score_spread":0.2076406604832747,"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."}}