{"id":"W2304191122","doi":"10.1109/ism.2015.118","title":"Human Action Recognition Using Hybrid Centroid Canonical Correlation Analysis","year":2015,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Histogram; Centroid; Computer science; Artificial intelligence; Canonical correlation; Fuse (electrical); Pyramid (geometry); Pattern recognition (psychology); Histogram of oriented gradients; Action recognition; Computer vision; Displacement (psychology); Action (physics); Set (abstract data type); Image (mathematics); Mathematics; Class (philosophy); 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":[0.0006795356,0.001457499,0.00133818,0.003532486,0.0005251454,0.0009427238,0.0009383024,0.0005435455,0.002611479],"category_scores_gemma":[0.001808305,0.0002659644,0.001174664,0.002867937,0.0005461276,0.001009382,0.001345662,0.000688427,0.001126209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006052099,"about_ca_system_score_gemma":0.00131662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0118018,"about_ca_topic_score_gemma":0.01709446,"domain_scores_codex":[0.9986986,0.0001883075,0.00005371843,0.0004350324,0.0004776523,0.0001466834],"domain_scores_gemma":[0.9992802,0.00008576649,0.00006636723,0.0001021451,0.00040634,0.00005927769],"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":[0.00024762,0.0001894635,0.006554923,0.0001778034,0.0002728708,0.0001976578,0.0001330744,0.0301203,0.02795368,0.00545133,0.01322492,0.9154764],"study_design_scores_gemma":[0.00002185868,0.0002040334,0.01550727,0.00004268244,0.0001333796,0.0004798653,0.000202757,0.9404196,0.02463531,0.00757396,0.0106639,0.0001154019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03502347,0.001563611,0.9553968,0.0001932101,0.0002164631,0.0001669297,0.0006218079,0.002334301,0.004483422],"genre_scores_gemma":[0.6267722,0.001835665,0.3607657,0.0002699156,0.0002757749,0.0003151375,0.002850431,0.0002980886,0.006617192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0118018,"threshold_uncertainty_score":0.02346623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1467698050957424,"score_gpt":0.3315174259305911,"score_spread":0.1847476208348488,"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."}}