{"id":"W2033747899","doi":"10.1007/s10846-012-9810-6","title":"Pipeline-Architecture Based Real-Time Active-Vision for Human-Action Recognition","year":2013,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Pipeline (software); Visibility; Computer science; Control reconfiguration; Artificial intelligence; Focus (optics); Computer vision; Action (physics); Architecture; Active vision; Real-time computing; Cognitive neuroscience of visual object recognition; Object (grammar); Engineering; Embedded system","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":[0.000616254,0.001119155,0.0008084266,0.0007521169,0.000408614,0.0008361987,0.002545094,0.001058455,0.007409134],"category_scores_gemma":[0.0007045718,0.0006288499,0.0006962015,0.0006440915,0.0003984814,0.001137853,0.0009446748,0.001146381,0.002534635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007750217,"about_ca_system_score_gemma":0.001684675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01208987,"about_ca_topic_score_gemma":0.01979324,"domain_scores_codex":[0.9996601,0.00003085829,0.00001551016,0.0001152994,0.0001067206,0.00007141734],"domain_scores_gemma":[0.9997173,0.00007097863,0.00002000153,0.00004701846,0.0001165616,0.00002814367],"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":[0.0007667956,0.0005783993,0.001429654,0.0002485955,0.0001711497,0.0001088455,0.0001170744,0.07948004,0.1280661,0.005863025,0.01165987,0.7715104],"study_design_scores_gemma":[0.00002042301,0.0001098602,0.0007561872,0.00000767312,0.00002536595,0.00004398059,0.00001123201,0.9729163,0.02142586,0.002357907,0.002307267,0.00001779418],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01141029,0.0002693562,0.9789397,0.00009398312,0.00008855949,0.00008108917,0.0002252268,0.007568956,0.001322882],"genre_scores_gemma":[0.4347298,0.0002932574,0.5555199,0.0002057223,0.0000649865,0.0002258209,0.001160967,0.0003567668,0.00744275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01208987,"threshold_uncertainty_score":0.024786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04918814858478357,"score_gpt":0.3074466190632438,"score_spread":0.2582584704784603,"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."}}