{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000452552,0.0001235968,0.0002236184,0.0001794819,0.00009933889,0.0002473761,0.0002483028,0.00007398237,0.00001489625],"category_scores_gemma":[0.00006259011,0.0001108788,0.00002948422,0.000198493,0.0001091453,0.001945846,0.00008953336,0.00009996245,0.000003879401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000166213,"about_ca_system_score_gemma":0.000189783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007159914,"about_ca_topic_score_gemma":0.00003549145,"domain_scores_codex":[0.9990079,0.00007392333,0.0003646245,0.0001711022,0.0002057648,0.0001766762],"domain_scores_gemma":[0.9992031,0.00009107058,0.000247894,0.0001506453,0.0002619241,0.0000453548],"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":[0.00007843107,0.0002990248,0.1423697,0.0002752092,0.0001373694,0.000002779987,0.005318391,0.01182161,0.02943027,0.01322856,0.00008664202,0.7969521],"study_design_scores_gemma":[0.000230108,0.000171152,0.01916213,0.00009511645,0.00001039797,0.00000660464,0.0001914082,0.9527498,0.0239617,0.002818363,0.000428154,0.0001750995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5285883,0.0000123452,0.4704016,0.00007050634,0.0001267937,0.00004478011,0.000006631606,0.00005027233,0.0006988063],"genre_scores_gemma":[0.8673622,0.000006089796,0.1324865,0.00004262817,0.00008265309,9.715955e-7,0.00001029597,0.000004017025,0.00000468133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9409282,"threshold_uncertainty_score":0.4521507,"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."}}