{"id":"W2145536551","doi":"10.1109/tcsvt.2008.928888","title":"Human Activity Recognition Based on Silhouette Directionality","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Silhouette; Artificial intelligence; Computer vision; Computer science; Activity recognition; Cluster analysis; Feature vector; Directionality; Pattern recognition (psychology); Background subtraction; Zoom; Feature extraction; Motion (physics); Feature (linguistics); Pixel; 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":[0.0002136542,0.0003704064,0.0003467628,0.001666217,0.0001299303,0.0005952851,0.0002519049,0.0002343004,0.001140084],"category_scores_gemma":[0.001384589,0.000196117,0.0002597195,0.0008855677,0.0002499908,0.0005409254,0.0002483418,0.0002570273,0.000763012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002105568,"about_ca_system_score_gemma":0.0001682368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001168599,"about_ca_topic_score_gemma":0.001748333,"domain_scores_codex":[0.9998013,0.00002545599,0.00001071372,0.00006069047,0.00007717196,0.00002474105],"domain_scores_gemma":[0.9994683,0.0001193506,0.0001041614,0.00006001062,0.0001981727,0.00005008493],"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":[0.0005321107,0.0000494723,0.01270443,0.0001723364,0.00005901942,0.0001358214,0.0002403113,0.01788421,0.2935536,0.00209559,0.001744252,0.6708289],"study_design_scores_gemma":[0.00003614334,0.000289473,0.08309427,0.00005267236,0.0000849659,0.001495681,0.0002077614,0.7065383,0.1951739,0.004965069,0.007964865,0.00009697332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1847479,0.0006722952,0.8084013,0.00007423139,0.00004559889,0.00007278661,0.0004588475,0.001904492,0.003622664],"genre_scores_gemma":[0.7569281,0.0007284517,0.2394378,0.00003197695,0.00004284305,0.00004521682,0.0007792848,0.0001356469,0.00187066],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001666217,"threshold_uncertainty_score":0.003813982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06953694203647971,"score_gpt":0.297580803117985,"score_spread":0.2280438610815053,"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."}}