{"id":"W2137307484","doi":"10.1109/icarcv.2004.1469792","title":"Motion detection and tracking system based on frame analysis and simulated static electric field (SSEF) snake","year":2005,"lang":"en","type":"article","venue":"","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Object detection; Tracking (education); Frame (networking); Video tracking; Edge detection; Object (grammar); Tracking system; Motion analysis; Monochromatic color; Motion estimation; Pattern recognition (psychology); Image (mathematics); Image processing; Kalman filter","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":[0.0002365801,0.0001107892,0.0001488863,0.0006031058,0.0001413087,0.0002522201,0.00009070522,0.0000833555,0.000009191283],"category_scores_gemma":[0.00004140634,0.00009790293,0.0000466312,0.001114281,0.000005705951,0.0003615743,0.00001867485,0.000129124,0.000003514341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006523383,"about_ca_system_score_gemma":0.000008508875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001611625,"about_ca_topic_score_gemma":0.00006177567,"domain_scores_codex":[0.9991229,0.0000732901,0.0001845516,0.0003053856,0.0001591804,0.0001547461],"domain_scores_gemma":[0.9994242,0.0001672572,0.00006738107,0.0002263921,0.00006382385,0.00005099953],"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":[0.0000202599,0.00004220429,0.0004305172,0.00004213101,0.00005729548,0.000005934301,0.0001416517,0.003736928,0.01445609,0.0001886411,0.00001235642,0.980866],"study_design_scores_gemma":[0.0001057305,0.0001652398,0.001880767,0.000007719489,0.00003476522,0.000004027479,0.00001038349,0.6414288,0.3562184,0.00004253618,0.00002392712,0.00007778042],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1332679,0.00002045475,0.8655803,0.0001677907,0.00002466663,0.0001219891,1.726341e-7,0.0004957223,0.0003210023],"genre_scores_gemma":[0.9907749,0.000005882058,0.008648863,0.0005053058,0.0000232955,0.000005714796,4.637404e-7,0.000005259388,0.000030267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9807882,"threshold_uncertainty_score":0.3992366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005290237175886794,"score_gpt":0.2250280522321376,"score_spread":0.2197378150562508,"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."}}