{"id":"W4391496273","doi":"10.1109/m2vip58386.2023.10413372","title":"Multi-Tracker Object Localizer: An Optimal Object Detector Based on Convolutional Neural Networks and Multi-Tracker Optimization Algorithm","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Minimum bounding box; Object (grammar); Object detection; Computer vision; Intersection (aeronautics); Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology); Process (computing); Algorithm; Image (mathematics); Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009597245,0.001109755,0.00118298,0.0009282115,0.0003416399,0.0007295363,0.001469642,0.00156378,0.001686655],"category_scores_gemma":[0.001209592,0.0006644211,0.0008107542,0.0007929349,0.0004841,0.001465699,0.001063237,0.0008516987,0.0006697751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009932948,"about_ca_system_score_gemma":0.001457088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005967265,"about_ca_topic_score_gemma":0.00731022,"domain_scores_codex":[0.9994395,0.0000751835,0.0000241749,0.000202771,0.0001856736,0.00007265766],"domain_scores_gemma":[0.9997093,0.00008013614,0.00005730235,0.00004162915,0.00008713766,0.00002443413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002556772,0.0001263601,0.002053432,0.0001552156,0.0001798706,0.0002078794,0.00007493507,0.4890048,0.03641997,0.007928494,0.004623812,0.4589697],"study_design_scores_gemma":[0.00000858795,0.00002974406,0.0003037557,0.000004301174,0.00001150142,0.00005566677,0.00000310922,0.9936016,0.004276674,0.000675571,0.001019826,0.000009545347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005149926,0.0002924298,0.9927549,0.00005674781,0.00002595041,0.00002295674,0.00002552544,0.001000113,0.0006714474],"genre_scores_gemma":[0.246131,0.0004564088,0.7465107,0.0002409692,0.00006021548,0.0001516704,0.0002902627,0.0003089605,0.005849689],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005967265,"threshold_uncertainty_score":0.01186508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02455707431407405,"score_gpt":0.2823179526317027,"score_spread":0.2577608783176287,"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."}}