{"id":"W1989730063","doi":"10.1109/iccrd.2011.5763846","title":"Hard hat detection in video sequences based on face features, motion and color information","year":2011,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"IntelliView Technologies (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Construct (python library); Face (sociological concept); Face detection; Computer vision; Artificial intelligence; Motion (physics); Video processing; Motion detection; Computer graphics (images); Facial recognition system; Feature extraction","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001522219,0.00007140463,0.00005783787,0.0001686656,0.0000909869,0.00008049559,0.0001469434,0.00006658416,0.00001225297],"category_scores_gemma":[0.00001292996,0.0000618427,0.00001763948,0.0002913981,0.00002258748,0.0009051213,0.00002902898,0.00008602421,0.00002226066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004480721,"about_ca_system_score_gemma":0.00001170906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003811908,"about_ca_topic_score_gemma":0.000107151,"domain_scores_codex":[0.9994952,0.00002330352,0.0001357195,0.0001508338,0.0001010045,0.000093935],"domain_scores_gemma":[0.9996641,0.00001881965,0.00006120745,0.0001863748,0.00003464236,0.00003483403],"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":[0.00004311221,0.0001036551,0.001369373,0.00002350042,0.000004112776,0.000001278881,0.001140758,0.0003734826,0.004804583,0.07246728,0.0003285839,0.9193403],"study_design_scores_gemma":[0.0003807881,0.0004389032,0.1980512,0.0000199378,0.000003377208,0.00001457922,0.000149087,0.5274997,0.2652705,0.004848951,0.003059311,0.0002636786],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03323657,0.000004334599,0.9608261,0.0003519994,0.00004461892,0.0002501864,9.501931e-7,0.0002617358,0.005023498],"genre_scores_gemma":[0.9664133,0.00000669376,0.0329446,0.0004488316,0.000005690171,0.00008612902,0.000001602535,0.000001921094,0.00009117835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9331768,"threshold_uncertainty_score":0.2521872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01698159410594511,"score_gpt":0.2181993283266093,"score_spread":0.2012177342206642,"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."}}