{"id":"W2973851319","doi":"10.3390/rs11182155","title":"Orientation- and Scale-Invariant Multi-Vehicle Detection and Tracking from Unmanned Aerial Videos","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer vision; Artificial intelligence; Vehicle tracking system; Kalman filter; Orientation (vector space); Video tracking; Object detection; Tracking (education); Video processing; Segmentation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004758084,0.0001314517,0.0001905389,0.00007497355,0.0002050258,0.0003758504,0.00008611878,0.000081914,0.000001468408],"category_scores_gemma":[0.00008931942,0.0001315427,0.00003099757,0.000195474,0.00003984045,0.0004553992,0.00008992818,0.0001301557,0.000009867374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002648816,"about_ca_system_score_gemma":0.00002379215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006430543,"about_ca_topic_score_gemma":0.0002697003,"domain_scores_codex":[0.998746,0.0001934647,0.0001989099,0.0004871428,0.0001574954,0.0002170067],"domain_scores_gemma":[0.9992065,0.0002526306,0.00009692446,0.0003022564,0.00006534906,0.00007637133],"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":[0.00002012323,0.000005327238,0.001256395,0.00001182764,0.00001343643,0.00001289549,0.001269746,0.00009615214,0.2249362,0.00004248664,5.434263e-7,0.7723349],"study_design_scores_gemma":[0.0009447535,0.00003959538,0.04281602,0.00007606832,0.000008958017,0.00004970505,0.00007772596,0.907176,0.04723382,0.001307407,0.00007304415,0.0001968694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5232614,0.00005091305,0.4759871,0.00008398542,0.0004332675,0.00008331636,6.025629e-7,0.00007126603,0.00002815513],"genre_scores_gemma":[0.7265556,0.0000126836,0.2731998,0.0001010546,0.0001102387,1.215078e-8,0.000001070644,0.000009797142,0.000009730624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9070799,"threshold_uncertainty_score":0.5364155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01972818863129574,"score_gpt":0.2705043076829735,"score_spread":0.2507761190516778,"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."}}