{"id":"W2130026429","doi":"10.1109/iccvw.2015.79","title":"The Visual Object Tracking VOT2015 Challenge Results","year":2015,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":705,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"Javna Agencija za Raziskovalno Dejavnost RS; European Commission","keywords":"BitTorrent tracker; Computer science; Benchmark (surveying); Artificial intelligence; Eye tracking; Object (grammar); Computer vision; Video tracking; Frame (networking); Tracking (education); Bounding overwatch; Term (time); Annotation; Visualization; Minimum bounding box; Object detection; Pattern recognition (psychology); Image (mathematics); Geography","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":[0.01095234,0.006410636,0.00342624,0.003534614,0.002192478,0.005358669,0.004230408,0.005151418,0.01389607],"category_scores_gemma":[0.02389531,0.000672654,0.00298534,0.002579789,0.001132894,0.003795097,0.005370838,0.0029517,0.014968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00303059,"about_ca_system_score_gemma":0.003537073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03120107,"about_ca_topic_score_gemma":0.03390812,"domain_scores_codex":[0.9874462,0.002518661,0.001083833,0.003466769,0.004175722,0.001308863],"domain_scores_gemma":[0.9917036,0.00178978,0.0004379567,0.00229246,0.00287627,0.0008998254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001176684,0.0006238305,0.002994181,0.001950217,0.0005440561,0.0003876209,0.0001430047,0.01614873,0.006171221,0.002984457,0.7857848,0.1810912],"study_design_scores_gemma":[0.001529935,0.002880216,0.02702844,0.001888534,0.0008117647,0.003634457,0.0008339669,0.2900465,0.03593654,0.02731468,0.6075777,0.0005172027],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.1155138,0.03861845,0.2356796,0.005224045,0.02499943,0.006946435,0.386041,0.094235,0.09274223],"genre_scores_gemma":[0.09588922,0.001995505,0.0724057,0.001506526,0.001165806,0.001545994,0.7944514,0.003325967,0.02771393],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03120107,"threshold_uncertainty_score":0.0620389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09512999012652176,"score_gpt":0.3695303862370754,"score_spread":0.2744003961105537,"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."}}