{"id":"W2085099937","doi":"10.1109/cvpr.2011.5995562","title":"Identifying players in broadcast sports videos using conditional random fields","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Conditional random field; Computer science; Artificial intelligence; Computer vision; Identification (biology); Tracking (education); Probabilistic logic; Tracking system; Kalman filter","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002834673,0.00007647496,0.0001334949,0.0001945524,0.00007162232,0.00008006157,0.0002380183,0.00005175392,0.0004265243],"category_scores_gemma":[0.00001951965,0.00006991047,0.00006608551,0.0003353667,0.00001545459,0.0006836439,0.0000778355,0.00007048096,0.0000127732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002372889,"about_ca_system_score_gemma":0.00003704602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003979677,"about_ca_topic_score_gemma":0.0001332244,"domain_scores_codex":[0.9990932,0.00003566214,0.0002609511,0.0002297152,0.0002287902,0.0001516664],"domain_scores_gemma":[0.9996178,0.00002885178,0.00006826108,0.0001970381,0.00004198891,0.00004606199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001189638,0.0005015967,0.5240432,0.00005330601,0.0001697827,0.0006211663,0.01264301,0.01554419,0.003798269,0.3804965,0.001744659,0.06026535],"study_design_scores_gemma":[0.001491963,0.00001947582,0.06434613,0.00004463185,0.00001958401,0.00002767613,0.0001911135,0.9104845,0.003064745,0.01982134,0.0001750762,0.0003137857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09776501,0.00002321292,0.8973863,0.00003133155,0.000133396,0.00005827832,4.14925e-7,0.00003620948,0.004565819],"genre_scores_gemma":[0.9751342,0.000006763446,0.02435795,0.0002712306,0.00002324021,0.000002703991,0.00000565326,0.000003271333,0.0001950112],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8949403,"threshold_uncertainty_score":0.4670142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05726826947129639,"score_gpt":0.2617551667420718,"score_spread":0.2044868972707755,"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."}}