{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001166318,0.001760026,0.001615784,0.00247229,0.0009114844,0.002994708,0.003356505,0.001462338,0.004304208],"category_scores_gemma":[0.005158238,0.001364692,0.001519199,0.002587568,0.001105741,0.002781763,0.003498212,0.001713252,0.0033986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009127011,"about_ca_system_score_gemma":0.001269515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009205351,"about_ca_topic_score_gemma":0.007618361,"domain_scores_codex":[0.9980525,0.0002344798,0.00009547707,0.0007841983,0.0006811466,0.0001523085],"domain_scores_gemma":[0.9977618,0.0007353571,0.0002729206,0.0007434884,0.0003600025,0.0001262996],"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":[0.0005420494,0.0001361217,0.003883994,0.00041004,0.0001879406,0.000315914,0.0004202956,0.1883726,0.02171528,0.01882344,0.02352721,0.7416651],"study_design_scores_gemma":[0.00003170546,0.0000460988,0.0005493484,0.00004540039,0.00002374752,0.0001900289,0.00004249073,0.9622453,0.01432081,0.00667862,0.01578875,0.00003770607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002785688,0.0002061519,0.9890844,0.00004946711,0.00006944333,0.0000641165,0.0003599262,0.006444755,0.0009359086],"genre_scores_gemma":[0.08161469,0.0004228297,0.9093775,0.0001615804,0.00008032316,0.0003055293,0.003091894,0.001941076,0.003004601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009205351,"threshold_uncertainty_score":0.01830351,"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."}}