{"id":"W2076424778","doi":"10.1145/2168752.2168760","title":"A Generic Approach for Systematic Analysis of Sports Videos","year":2012,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Probabilistic latent semantic analysis; Conditional random field; Artificial intelligence; Topic model; Bag-of-words model; Support vector machine; Pattern recognition (psychology); Categorization; Representation (politics); Field (mathematics); Probabilistic logic; Classifier (UML); Video content analysis; Histogram; Machine learning; Object (grammar); Video tracking; 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":[0.002163215,0.001486242,0.000880462,0.00682677,0.0007302096,0.002625949,0.001710898,0.001289033,0.004048966],"category_scores_gemma":[0.004395956,0.0005918528,0.002063158,0.003752951,0.001062128,0.002734504,0.002833772,0.001161523,0.00318318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008921484,"about_ca_system_score_gemma":0.001775141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002357608,"about_ca_topic_score_gemma":0.002781269,"domain_scores_codex":[0.9965263,0.0005785928,0.0003086312,0.001321298,0.001105947,0.000159174],"domain_scores_gemma":[0.9982197,0.0002482885,0.0002151454,0.0005627258,0.0006652417,0.00008895926],"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":[0.000158305,0.0002074005,0.004133647,0.001216126,0.00024817,0.0004734784,0.0008973566,0.007259863,0.07272127,0.04700548,0.01461207,0.8510669],"study_design_scores_gemma":[0.00009448533,0.000665627,0.02403571,0.0009363169,0.0004153497,0.004248395,0.002001725,0.4216637,0.1048833,0.1278819,0.3128096,0.0003639213],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002099005,0.0004501553,0.9919006,0.00009212223,0.00004027402,0.0003800865,0.0006597969,0.002406392,0.00197159],"genre_scores_gemma":[0.03829206,0.0007226525,0.9538846,0.0001258863,0.00007554473,0.0006556036,0.002825874,0.0002975389,0.003120317],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00682677,"threshold_uncertainty_score":0.01354516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02350077246559766,"score_gpt":0.2522775301339221,"score_spread":0.2287767576683244,"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."}}