{"id":"W2087466987","doi":"","title":"Automated Collection of Pedestrian Data Using Computer Vision Techniques","year":2009,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pedestrian; Computer science; Artificial intelligence; Computer vision; Pedestrian detection; Feature (linguistics); Data collection; Video tracking; Field (mathematics); Frame (networking); Object (grammar); Transport engineering; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002007362,0.0003059175,0.0004706789,0.0006050722,0.0002541657,0.0002621131,0.001954931,0.0002700241,0.000003643778],"category_scores_gemma":[0.0001252449,0.0003050754,0.0001152242,0.001491669,0.00006366168,0.001234865,0.0005556915,0.000300483,0.00000255248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002329261,"about_ca_system_score_gemma":0.0003132745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002585985,"about_ca_topic_score_gemma":0.0003130244,"domain_scores_codex":[0.9972308,0.0003745911,0.0006453738,0.0007172365,0.0004377981,0.0005942088],"domain_scores_gemma":[0.9968074,0.0001523328,0.000400503,0.002259168,0.0001964942,0.0001841138],"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":[0.0001345717,0.0008265843,0.01807103,0.00008132149,0.00008198408,0.0001388304,0.0003120039,0.003078222,0.09704769,0.009793602,0.007200332,0.8632338],"study_design_scores_gemma":[0.0002761542,0.0004385809,0.05089343,0.00009774197,0.00001498171,0.0001757032,0.000004029971,0.9164368,0.02807716,0.002566928,0.0007023969,0.0003160738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0400722,0.0002953846,0.9544216,0.001212638,0.0001796461,0.0004914881,0.00001743238,0.003137513,0.0001720992],"genre_scores_gemma":[0.4270232,0.0000620427,0.5722719,0.0004981766,0.00008503092,0.000009629266,0.0000178484,0.00001646989,0.000015722],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9133586,"threshold_uncertainty_score":0.9999402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03171661749784266,"score_gpt":0.3136074253092418,"score_spread":0.2818908078113992,"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."}}