{"id":"W4388024018","doi":"10.18280/ts.400503","title":"Joint Solution for Temporal-Spatial Synchronization of Multi-View Videos and Pedestrian Matching in Crowd Scenes","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Joint (building); Pedestrian; Computer science; Matching (statistics); Computer vision; Artificial intelligence; Synchronization (alternating current); Geography; Mathematics; Engineering; Telecommunications; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0007615025,0.0007813143,0.0008845849,0.0005811116,0.0003229026,0.0006425085,0.001040881,0.001077082,0.001328424],"category_scores_gemma":[0.002337572,0.0004510715,0.0006104693,0.0006127929,0.0004422535,0.0007716678,0.00112097,0.0006845598,0.0002719552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005685041,"about_ca_system_score_gemma":0.001320446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007390502,"about_ca_topic_score_gemma":0.005404693,"domain_scores_codex":[0.999642,0.00006139316,0.0000149343,0.0001320707,0.00008789771,0.00006162004],"domain_scores_gemma":[0.9995828,0.0001416405,0.0001005275,0.00003716825,0.00008986592,0.00004797056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000277819,0.00007782668,0.002204833,0.0001459083,0.00007778163,0.0002354483,0.0002264167,0.8330576,0.01358537,0.0135161,0.001987437,0.1346076],"study_design_scores_gemma":[0.00000409256,0.00001859501,0.0002232647,0.000002787752,0.000004458378,0.00001843655,0.00001605974,0.9975986,0.0007575593,0.001095835,0.0002561403,0.000004282313],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02159165,0.00009042457,0.9773183,0.00006334049,0.00003185609,0.00002444414,0.00003551263,0.0001308887,0.0007135971],"genre_scores_gemma":[0.7743805,0.0002381257,0.2203935,0.00008565284,0.0000947879,0.0001406718,0.0002551752,0.00008309251,0.004328464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007390502,"threshold_uncertainty_score":0.01469499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0627574904598235,"score_gpt":0.3100703147067267,"score_spread":0.2473128242469032,"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."}}