{"id":"W2619326953","doi":"10.1109/ivs.2017.7995781","title":"Detection and recognition of traffic signs inside the attentional visual field of drivers","year":2017,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer vision; Gaze; Artificial intelligence; Computer science; Advanced driver assistance systems; Traffic sign recognition; Histogram; Support vector machine; Histogram of oriented gradients; Feature extraction; Classifier (UML); Visual field; Feature (linguistics); Traffic sign; Pattern recognition (psychology); Sign (mathematics); Mathematics; Psychology","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":[0.0002339838,0.0002579021,0.0002753217,0.001120387,0.0001713662,0.0004373993,0.0002603502,0.0003270875,0.0009023969],"category_scores_gemma":[0.001003325,0.0001357458,0.0002003442,0.0002419937,0.0002044342,0.0004048165,0.0003738619,0.0002352412,0.0003775388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001405606,"about_ca_system_score_gemma":0.0002749198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001524632,"about_ca_topic_score_gemma":0.002681224,"domain_scores_codex":[0.9998482,0.00002497749,0.000006539094,0.0000325486,0.00005677545,0.0000309242],"domain_scores_gemma":[0.999455,0.0001200953,0.0001046291,0.00004523498,0.0002325784,0.00004249964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003032386,0.00009044381,0.02229944,0.0001751965,0.00004352855,0.0003771915,0.000262331,0.002686805,0.577238,0.001082527,0.00139301,0.3940483],"study_design_scores_gemma":[0.0000713485,0.001578656,0.2653415,0.0001627136,0.0002774585,0.006188136,0.0007741627,0.1878442,0.5194488,0.004054099,0.01405698,0.0002020058],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7883694,0.001265398,0.2050938,0.0001148132,0.0001008365,0.00007755066,0.0001591353,0.0008316248,0.003987473],"genre_scores_gemma":[0.960654,0.0004624486,0.03713037,0.00004998876,0.00005415987,0.00002679403,0.000113519,0.00002865105,0.001480081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001524632,"threshold_uncertainty_score":0.003031492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02663490395182042,"score_gpt":0.277146594244267,"score_spread":0.2505116902924466,"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."}}