{"id":"W4387961473","doi":"10.1145/3606038.3616162","title":"Jersey Number Recognition using Keyframe Identification from Low-Resolution Broadcast Videos","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Identification (biology); Motion blur; Computer vision; Transformer; Context (archaeology); Analytics; Pattern recognition (psychology); Data mining; 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":[0.0002226082,0.00098644,0.0005995441,0.002386744,0.0003067822,0.0007101599,0.0006971738,0.0005123954,0.002399103],"category_scores_gemma":[0.0009368062,0.0001835831,0.000434501,0.001052384,0.0002110599,0.001037073,0.000580389,0.0005608887,0.002429352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004065777,"about_ca_system_score_gemma":0.0005434831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007202787,"about_ca_topic_score_gemma":0.01824424,"domain_scores_codex":[0.9997573,0.0000148898,0.00001271034,0.00008612579,0.00007472533,0.00005416925],"domain_scores_gemma":[0.9997419,0.00004429765,0.00004419571,0.00003793336,0.0001063949,0.0000252628],"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":[0.0008145403,0.0002541062,0.00560239,0.0002940882,0.00008104853,0.0004342783,0.0001019609,0.01433521,0.1367838,0.00207419,0.0153395,0.8238849],"study_design_scores_gemma":[0.00005686202,0.0004338197,0.02497878,0.0001138654,0.0001824206,0.0009827261,0.000406707,0.7966892,0.1472069,0.00520212,0.02367741,0.00006913573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2435404,0.001926794,0.7241093,0.000306584,0.000593808,0.0004708038,0.004210821,0.009693191,0.01514838],"genre_scores_gemma":[0.6613846,0.002297588,0.3087324,0.0002076378,0.0003071361,0.0001882879,0.0137854,0.0003275061,0.01276936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007202787,"threshold_uncertainty_score":0.01432168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04108792256306396,"score_gpt":0.2752439668001994,"score_spread":0.2341560442371354,"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."}}