{"id":"W2929588561","doi":"10.1155/2019/9060797","title":"Extracting Vehicle Trajectories Using Unmanned Aerial Vehicles in Congested Traffic Conditions","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Computer science; Trajectory; Convolutional neural network; Feature (linguistics); Traffic congestion; Artificial intelligence; Feature extraction; Real-time computing; Tracking (education); Computer vision; Simulation; Engineering; Transport engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000100386,0.0005189372,0.0001949659,0.0009046997,0.0001811625,0.0002549899,0.0002621646,0.0002346346,0.0003132298],"category_scores_gemma":[0.0004823035,0.0001462068,0.0001948542,0.0005014887,0.0001396355,0.0004725721,0.0002622876,0.0002308115,0.0001470914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003626067,"about_ca_system_score_gemma":0.0004214332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02213635,"about_ca_topic_score_gemma":0.02231948,"domain_scores_codex":[0.9998952,0.000008368226,0.000003975887,0.00003434335,0.00003188875,0.00002615269],"domain_scores_gemma":[0.9998564,0.00002169947,0.00003894883,0.0000173913,0.00004862655,0.00001694375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003218943,0.0001180523,0.06029861,0.0001397456,0.0001163592,0.001144402,0.0003267496,0.5730677,0.04013396,0.001862179,0.002853747,0.3196166],"study_design_scores_gemma":[0.000005089657,0.00003871636,0.01757794,0.000008897402,0.00001366913,0.000086609,0.0001336597,0.9717916,0.008801658,0.0005704426,0.0009602791,0.00001152774],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8866954,0.0002871186,0.1087061,0.00008190313,0.00005190924,0.00004166345,0.0007125405,0.001259064,0.002164284],"genre_scores_gemma":[0.9788641,0.00008962982,0.01982648,0.0000112143,0.000006251238,0.000009941588,0.0006665866,0.00002016874,0.0005056334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02213635,"threshold_uncertainty_score":0.04401505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02186146603190751,"score_gpt":0.3077969169988709,"score_spread":0.2859354509669633,"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."}}