{"id":"W2900883900","doi":"10.1051/matecconf/201823202046","title":"Visual Target Tracking using Robust Information Interaction between Single Tracker and Online Model","year":2018,"lang":"en","type":"article","venue":"MATEC Web of Conferences","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"State Key Laboratory of Networking and Switching Technology; China Scholarship Council; Beijing University of Posts and Telecommunications; National Natural Science Foundation of China","keywords":"Discriminative model; Computer science; BitTorrent tracker; Artificial intelligence; Particle filter; Sigmoid function; Support vector machine; Computer vision; Eye tracking; Classifier (UML); Pattern recognition (psychology); Histogram; Maximum a posteriori estimation; A priori and a posteriori; Tracking (education); Filter (signal processing); Mathematics; Maximum likelihood; Artificial neural network; Image (mathematics)","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":[0.001324961,0.0009403357,0.001908263,0.0008808189,0.0005650591,0.001305181,0.002173154,0.001348356,0.001162531],"category_scores_gemma":[0.003147666,0.0006845601,0.0009822068,0.001038017,0.0005719818,0.002487992,0.002183061,0.001168159,0.0008885768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007756467,"about_ca_system_score_gemma":0.001073042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002959491,"about_ca_topic_score_gemma":0.002358198,"domain_scores_codex":[0.9985819,0.0001772475,0.00005734416,0.0005376092,0.0005081068,0.0001377935],"domain_scores_gemma":[0.9988794,0.0003156782,0.0001799995,0.0003222605,0.0002433577,0.0000592763],"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":[0.000357785,0.0002314089,0.002099373,0.000132719,0.0001582964,0.0002415592,0.000275089,0.2641318,0.05226901,0.01213862,0.00302659,0.6649377],"study_design_scores_gemma":[0.00001005553,0.00005793582,0.0003241308,0.000004596383,0.00001924389,0.0000941091,0.00001002252,0.990595,0.005510715,0.002210725,0.001149968,0.00001344347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005205024,0.0001122535,0.9933921,0.00003416602,0.00002087265,0.00001308596,0.00001087057,0.0006821351,0.0005295239],"genre_scores_gemma":[0.5454601,0.0002648575,0.4495153,0.0001830527,0.0001089611,0.0001574902,0.0002407947,0.0002350846,0.003834337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002959491,"threshold_uncertainty_score":0.007007182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1053658872287972,"score_gpt":0.3397914562043101,"score_spread":0.2344255689755128,"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."}}