{"id":"W4402727422","doi":"10.1109/mwscas60917.2024.10658879","title":"Pedestrian and Cyclist Object Detection Using Thermal and Dash Cameras in Different Weather Conditions","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dash; Pedestrian; Computer science; Computer vision; Object detection; Object (grammar); Artificial intelligence; Pedestrian detection; Environmental science; Meteorology; Remote sensing; Computer graphics (images); Transport engineering; Engineering; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004003448,0.0008136185,0.0005376173,0.001110044,0.0002965177,0.0005405328,0.0004089429,0.0004831218,0.00151478],"category_scores_gemma":[0.0009574111,0.000195161,0.0003731276,0.0005199041,0.0002511403,0.0006010764,0.0006555673,0.0004756588,0.0006330518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003295976,"about_ca_system_score_gemma":0.0003123501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009244548,"about_ca_topic_score_gemma":0.02444749,"domain_scores_codex":[0.9995577,0.00005068952,0.00001813876,0.0001749864,0.00009180523,0.0001067121],"domain_scores_gemma":[0.9996743,0.0000567718,0.00003938041,0.00004470222,0.0001313435,0.00005361413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004554559,0.001134461,0.2988253,0.00141949,0.0007677257,0.00161302,0.0008292936,0.0592556,0.1352446,0.001179473,0.02619524,0.4689813],"study_design_scores_gemma":[0.00008969319,0.001134164,0.5383936,0.000215926,0.0003258738,0.001580545,0.001290005,0.3681852,0.07692438,0.001104631,0.01063639,0.0001194809],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9716495,0.0005087569,0.01624689,0.0001004702,0.0001471304,0.0001007825,0.004813155,0.00123906,0.005194235],"genre_scores_gemma":[0.971219,0.000246888,0.01580073,0.00007450486,0.00004356573,0.00005086705,0.009761371,0.00005812801,0.002745043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009244548,"threshold_uncertainty_score":0.01838148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02672324224501671,"score_gpt":0.3123374237750437,"score_spread":0.285614181530027,"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."}}