{"id":"W15976199","doi":"10.1213/01.ane.0000155260.93406.29","title":"Automatic camera control using unobtrusive vision and audio tracking","year":2010,"lang":"en","type":"article","venue":"Graphics Interface","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"National Heart, Lung, and Blood Institute","keywords":"Computer science; Video production; Video tracking; Variety (cybernetics); Video processing; Tracking (education); Post-production; Multimedia; Computer vision; Key (lock); Quality (philosophy); Artificial intelligence; Video quality; Video camera; Engineering","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":[0.001261693,0.001180442,0.0006619488,0.001420681,0.0003425802,0.0008236403,0.001249776,0.0009517636,0.008886059],"category_scores_gemma":[0.003822778,0.0004465132,0.0004128443,0.0006415876,0.0004393156,0.0008525532,0.001212599,0.0004663868,0.001901242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003908752,"about_ca_system_score_gemma":0.0006264801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002434803,"about_ca_topic_score_gemma":0.002031496,"domain_scores_codex":[0.9986248,0.0002314918,0.00006267365,0.0004610487,0.0005053908,0.000114704],"domain_scores_gemma":[0.9983299,0.0006749682,0.0001718711,0.0002372341,0.0004996397,0.0000864589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002048,0.0004921606,0.00612583,0.0004315347,0.00008680749,0.0002866727,0.00036156,0.007543154,0.1684687,0.001196621,0.004277457,0.8086814],"study_design_scores_gemma":[0.00167253,0.006844698,0.06917274,0.0004094961,0.0005869563,0.004634505,0.0004356104,0.5952991,0.2518613,0.004595865,0.06393391,0.0005533242],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1019935,0.0009561459,0.8780388,0.0001539538,0.0003992565,0.001017267,0.0004238405,0.006658714,0.01035858],"genre_scores_gemma":[0.6968576,0.0005605198,0.2914635,0.0002190307,0.00015504,0.0009745829,0.000425055,0.0003388153,0.009005879],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008886059,"threshold_uncertainty_score":0.02972686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0113881727990213,"score_gpt":0.2750508403293684,"score_spread":0.2636626675303471,"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."}}