{"id":"W2893295587","doi":"10.1109/access.2018.2871659","title":"Adaptive Framework for Robust Visual Tracking","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Robustness (evolution); BitTorrent tracker; Video tracking; Eye tracking; Trajectory; Particle filter; Tracking system; Benchmark (surveying); Object detection; Object (grammar); Pattern recognition (psychology); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001870908,0.001412363,0.001395126,0.001612358,0.0004403514,0.001180868,0.003094075,0.001534498,0.002914425],"category_scores_gemma":[0.002897036,0.0005422832,0.001392911,0.001558293,0.0008013212,0.001223416,0.001994935,0.001913587,0.001588455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00097562,"about_ca_system_score_gemma":0.001575341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009972547,"about_ca_topic_score_gemma":0.005772185,"domain_scores_codex":[0.9983135,0.0002665075,0.00006870074,0.0005427423,0.0006406952,0.0001677923],"domain_scores_gemma":[0.9993094,0.0001752215,0.0000838789,0.0001284609,0.0002563331,0.00004671686],"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":[0.0001620259,0.00009918262,0.0005426796,0.0001393046,0.0001309791,0.0001618393,0.00009154436,0.5399038,0.0139675,0.03928738,0.006329384,0.3991843],"study_design_scores_gemma":[0.000009826193,0.00002889649,0.00008756603,0.000004881922,0.000009145786,0.00003798206,0.000004715765,0.9925014,0.000980212,0.004180151,0.002145683,0.000009582837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000720128,0.0001725683,0.9980993,0.00002356232,0.00002726263,0.00002112918,0.00002343628,0.0004937727,0.000418907],"genre_scores_gemma":[0.1984093,0.0008031223,0.7927516,0.0002094978,0.0002068171,0.000379917,0.0006435761,0.0003500163,0.006246154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009972547,"threshold_uncertainty_score":0.01982898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.146895719853097,"score_gpt":0.420419875378432,"score_spread":0.273524155525335,"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."}}