{"id":"W4387790368","doi":"10.11834/jig.220026","title":"Short-term memory and CenterTrack based vehicle-related multi-target tracking method","year":2023,"lang":"en","type":"article","venue":"Journal of Image and Graphics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Vehicle tracking system; Tracking (education); Artificial intelligence; Computer vision; Video tracking; Adaptability; Key (lock); Tracking system; Metric (unit); Real-time computing; Trajectory; Term (time); Intelligent transportation system; Object detection; Object (grammar); Pattern recognition (psychology); Engineering; Computer security; Kalman filter","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001106121,0.00072803,0.0008014972,0.0009466467,0.0009430319,0.001624293,0.001484845,0.00102983,0.003237311],"category_scores_gemma":[0.003067144,0.0003088047,0.0005168966,0.001442374,0.0006268104,0.003392876,0.001343328,0.0009294775,0.0008533163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008034012,"about_ca_system_score_gemma":0.001731007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009093218,"about_ca_topic_score_gemma":0.005370378,"domain_scores_codex":[0.9990718,0.000122305,0.00005487068,0.0003530137,0.0003015675,0.00009632281],"domain_scores_gemma":[0.9990442,0.0002960403,0.0000883687,0.00009461502,0.0004300282,0.000046604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00094061,0.0002341582,0.0170026,0.0006068171,0.00032605,0.0003447537,0.0009237175,0.1500664,0.01897969,0.03440499,0.01218733,0.7639828],"study_design_scores_gemma":[0.0000933849,0.0002994787,0.006931611,0.00005105113,0.0001933128,0.0004614015,0.0004266123,0.9502193,0.01448374,0.01628365,0.01045785,0.00009869866],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07775167,0.002868198,0.89996,0.0004772429,0.0003932681,0.0001466117,0.0002866317,0.001355802,0.01676057],"genre_scores_gemma":[0.8278041,0.002078741,0.1492313,0.0003954889,0.000241778,0.0002257717,0.0006861477,0.0001206284,0.01921603],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009093218,"threshold_uncertainty_score":0.01808059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03653671942401341,"score_gpt":0.330714434326764,"score_spread":0.2941777149027505,"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."}}