{"id":"W2946675038","doi":"10.1007/978-3-030-27202-9_17","title":"Tracking in Urban Traffic Scenes from Background Subtraction and Object Detection","year":2019,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Background subtraction; Artificial intelligence; Computer science; Computer vision; Object detection; Trajectory; Tracking (education); Object (grammar); Video tracking; Task (project management); Subtraction; Detector; Class (philosophy); Pattern recognition (psychology); Pixel; Mathematics; Engineering; Psychology","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002467101,0.0004696923,0.000607675,0.0009542605,0.0001838386,0.001437045,0.00176616,0.0004218876,0.000002249735],"category_scores_gemma":[0.0001930941,0.0004634348,0.0001012632,0.001712836,0.0002528192,0.001168559,0.001192285,0.001429758,0.000007920367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003637621,"about_ca_system_score_gemma":0.0003910887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007233185,"about_ca_topic_score_gemma":0.002059526,"domain_scores_codex":[0.9955643,0.0004103197,0.0005662212,0.002103653,0.0006745319,0.0006809976],"domain_scores_gemma":[0.9970285,0.001212951,0.0002998546,0.001214676,0.0001353227,0.0001087511],"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":[0.000009638424,0.00004333975,0.01484799,0.00004753794,0.000005761876,0.00002344708,0.00150535,0.2682348,0.001513387,0.00001069829,4.491778e-7,0.7137576],"study_design_scores_gemma":[0.0003361964,0.00007012246,0.2037555,0.0003351124,0.000004358819,0.00002693807,0.000001926454,0.7798044,0.008358307,0.006806806,0.00001479444,0.0004854975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.447488,0.000546799,0.5485539,0.0001491102,0.002910201,0.0002411747,0.000001786079,0.0001046221,0.000004393856],"genre_scores_gemma":[0.804957,0.00006906683,0.1943884,0.0001979411,0.0003527428,0.00001348644,0.000003726465,0.00001725049,3.53126e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7132721,"threshold_uncertainty_score":0.9997817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0329522397993024,"score_gpt":0.3023294807574394,"score_spread":0.269377240958137,"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."}}