{"id":"W4389041073","doi":"10.1109/icce-asia59966.2023.10326403","title":"A Deep Multi-Object Tracking Technique in Swimming Video Scenes","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Research Foundation","keywords":"Computer vision; Video tracking; Artificial intelligence; Tracking (education); Computer science; Object (grammar); Set (abstract data type); Track (disk drive); Feature (linguistics)","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":[0.0007089745,0.000769862,0.0007531734,0.001267023,0.0007193962,0.0007877936,0.001015274,0.0008626599,0.0009344906],"category_scores_gemma":[0.0008898011,0.0003648436,0.0008947141,0.001342448,0.0003936471,0.001409027,0.001129247,0.001,0.0004842913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005141101,"about_ca_system_score_gemma":0.001054229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01084739,"about_ca_topic_score_gemma":0.01277582,"domain_scores_codex":[0.9996039,0.00003115137,0.0000217091,0.0001619842,0.0001134188,0.00006785287],"domain_scores_gemma":[0.9997644,0.00003173318,0.00002527961,0.00004966099,0.00009854007,0.00003036176],"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":[0.0001441762,0.0001014565,0.002397811,0.0001087143,0.00009878143,0.0001659207,0.0001617359,0.0401358,0.09186623,0.003494746,0.002728149,0.8585966],"study_design_scores_gemma":[0.00001346057,0.0001359747,0.003372992,0.00002273659,0.00004875107,0.0002254069,0.00005137019,0.9613944,0.02761295,0.00197527,0.005115652,0.00003116871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0174778,0.0003596031,0.9802393,0.00007803589,0.00007511749,0.00005170751,0.00005517496,0.0007762769,0.0008869436],"genre_scores_gemma":[0.2854114,0.0007849188,0.7049522,0.000240663,0.00007632591,0.0001111144,0.0005510249,0.0001519602,0.007720563],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01084739,"threshold_uncertainty_score":0.02156848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05265429137839961,"score_gpt":0.3342538130707993,"score_spread":0.2815995216923997,"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."}}