{"id":"W2907567415","doi":"10.1109/cjece.2018.2875142","title":"Mean Shift Tracker With Grey Prediction for Visual Object Tracking","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Bhattacharyya distance; Computer vision; Tracking (education); Artificial intelligence; Eye tracking; Particle filter; Video tracking; Computer science; Object (grammar); Mean-shift; Computation; Mathematics; Filter (signal processing); Pattern recognition (psychology); Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004050153,0.000128901,0.0002062274,0.0002876203,0.0001239868,0.0002240882,0.0002457779,0.00005521443,0.000001423251],"category_scores_gemma":[0.00004398753,0.0001050298,0.00005634321,0.0003588855,0.00002770653,0.0003868187,0.000009714753,0.0001932876,5.24503e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005316959,"about_ca_system_score_gemma":0.0002338882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006704008,"about_ca_topic_score_gemma":0.0004015727,"domain_scores_codex":[0.9990732,0.00002779565,0.0002388261,0.0001777158,0.0001251786,0.0003572727],"domain_scores_gemma":[0.9990994,0.0001650638,0.00007666304,0.00009487481,0.0001985811,0.0003654397],"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":[0.00009509524,0.00006458848,0.03101433,0.00009006088,0.0003708505,0.0003379112,0.003257936,0.008266972,0.0006039937,0.0182234,0.0007464082,0.9369285],"study_design_scores_gemma":[0.001288245,0.003916661,0.1790268,0.0001887217,0.0000376115,0.001220782,0.000005053074,0.8039543,0.001990369,0.001200037,0.006726895,0.0004445337],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1399807,0.0002635381,0.8591068,0.0001333808,0.0004104825,0.00006287282,0.000001116142,0.00002614414,0.00001491561],"genre_scores_gemma":[0.921928,0.000004224074,0.07709905,0.0001072475,0.0008440193,0.000001746387,4.630293e-7,0.00001186414,0.000003401926],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9364839,"threshold_uncertainty_score":0.428299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01122335391793357,"score_gpt":0.2271412955726198,"score_spread":0.2159179416546863,"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."}}