{"id":"W3210339021","doi":"10.1109/ccece53047.2021.9569159","title":"GroupNet: Detecting the Social Distancing Violation using Object Tracking in Crowdscene","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Social distance; Computer science; Artificial intelligence; Object (grammar); Computer vision; Object detection; Euclidean distance; Distance matrix; False positive paradox; Group (periodic table); Pattern recognition (psychology); Algorithm","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":[0.001435484,0.000104851,0.0001488606,0.00005783112,0.0004201072,0.0003291999,0.0002831549,0.00005433118,0.000008242354],"category_scores_gemma":[0.0002134568,0.00008418277,0.00007094604,0.0008523371,0.00001816103,0.0004801755,0.0001268199,0.0002117965,0.00000206766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009283488,"about_ca_system_score_gemma":0.00008080392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001431972,"about_ca_topic_score_gemma":0.0009162938,"domain_scores_codex":[0.9985622,0.000362593,0.0002605955,0.0003157844,0.0002056013,0.000293225],"domain_scores_gemma":[0.9992232,0.0003407198,0.0000868259,0.0002502572,0.00007787496,0.00002114296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001082556,0.00006987312,0.1420137,0.0000432907,0.00003185362,0.0001536026,0.01119751,0.004602405,0.144195,0.01038687,0.00001061465,0.6872845],"study_design_scores_gemma":[0.0005704006,0.00001681181,0.573181,0.00009664064,0.000008634638,0.00008795375,0.0009966961,0.3564574,0.06221143,0.005768325,0.0001672887,0.0004374171],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3977904,0.00009697618,0.6008691,0.0001837857,0.0002174475,0.00004064171,1.466328e-7,0.00006396028,0.0007375228],"genre_scores_gemma":[0.9476964,0.000002995745,0.05192006,0.0001954984,0.0001583844,0.000002383135,5.697098e-7,0.000008910168,0.00001482327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6868471,"threshold_uncertainty_score":0.3432874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05370404446308074,"score_gpt":0.3446308968926753,"score_spread":0.2909268524295946,"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."}}