{"id":"W2149454580","doi":"10.1109/crv.2006.3","title":"A feature-based tracking algorithm for vehicles in intersections","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":226,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Feature (linguistics); Computer science; Tracking (education); Intelligent transportation system; Track (disk drive); Computer vision; Algorithm; Field (mathematics); Artificial intelligence; Extension (predicate logic); Vehicle tracking system; Engineering; Transport engineering; Mathematics; Segmentation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004483532,0.00008596892,0.0001150581,0.0001535159,0.00006966454,0.0001489756,0.0002645577,0.00005070824,0.000002582838],"category_scores_gemma":[0.00002520516,0.00007749884,0.0000760755,0.000367621,0.00001626739,0.0002286384,0.00002341523,0.00009078144,0.000003740586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003397489,"about_ca_system_score_gemma":0.00003582892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001884476,"about_ca_topic_score_gemma":0.0005256712,"domain_scores_codex":[0.9992629,0.00005508798,0.0001202388,0.0002525416,0.00009072238,0.0002185146],"domain_scores_gemma":[0.9994162,0.0002620657,0.00003449406,0.000213198,0.00005156299,0.00002248634],"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":[0.000003865056,0.0001032875,0.006326188,0.000009484173,0.000005088199,0.000009605753,0.00006104536,0.001050398,0.0008535809,0.01379576,0.001804779,0.9759769],"study_design_scores_gemma":[0.001256051,0.00009849057,0.06727836,0.0000394561,0.000003273091,0.00001131197,0.00002387658,0.8803529,0.01692718,0.01853324,0.0151693,0.000306563],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003027199,0.00005401646,0.9936989,0.001379624,0.0002775661,0.0001401231,0.00000211635,0.0001760419,0.001244401],"genre_scores_gemma":[0.3856295,4.186697e-7,0.6137369,0.0002584972,0.00007316314,0.0000314459,0.000002646642,0.000005726075,0.0002616823],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9756703,"threshold_uncertainty_score":0.3160311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02344892529384042,"score_gpt":0.2932022787556262,"score_spread":0.2697533534617857,"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."}}