{"id":"W3208050295","doi":"10.48550/arxiv.2111.01606","title":"PolyTrack: Tracking with Bounding Polygons","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Minimum bounding box; Computer science; Bounding overwatch; Polygon (computer graphics); Computer vision; Artificial intelligence; Segmentation; Tracking (education); Offset (computer science); Frame (networking); Kalman filter; Video tracking; Object (grammar); Image (mathematics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006272076,0.0004444581,0.0005335818,0.0003187186,0.0003323359,0.0007483351,0.001914475,0.000316865,0.00002554805],"category_scores_gemma":[0.00006412183,0.0004770783,0.0002748159,0.001095625,0.0001323858,0.0008391502,0.001384394,0.000996692,0.00002474393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002057024,"about_ca_system_score_gemma":0.0005621928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002332735,"about_ca_topic_score_gemma":0.0002799093,"domain_scores_codex":[0.9970055,0.0003648506,0.0002413889,0.001589414,0.0001734104,0.0006254398],"domain_scores_gemma":[0.9971921,0.0002481869,0.0003066879,0.001784783,0.0002391183,0.0002291132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001730394,0.0007919493,0.1743421,0.0007168654,0.00155158,0.01816843,0.004845735,0.3381078,0.001322745,0.4200951,0.0002890888,0.03959553],"study_design_scores_gemma":[0.007478456,0.000924672,0.2440639,0.005370732,0.001170611,0.001068627,0.003417978,0.5575099,0.01595637,0.1467354,0.004134522,0.01216877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3393678,0.0001781624,0.6562598,0.0001387653,0.0006747392,0.0001368003,0.000004903858,0.0003481627,0.002890856],"genre_scores_gemma":[0.9726799,0.0001303811,0.02624789,0.0001483903,0.0001290031,8.998761e-7,0.00001507022,0.00003549151,0.0006130064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.633312,"threshold_uncertainty_score":0.9997681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1037332671745549,"score_gpt":0.2151535508388807,"score_spread":0.1114202836643258,"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."}}