{"id":"W7100777480","doi":"","title":"A Co-inference Approach to Robust Visual Tracking","year":2001,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tracking (education); Representation (politics); Probabilistic logic; Eye tracking; Curse of dimensionality; Graphical model; Robustness (evolution); Tracking system; Process (computing)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002784213,0.000883287,0.001752959,0.001619838,0.0007654367,0.001408271,0.00328373,0.00196708,0.003218869],"category_scores_gemma":[0.006641496,0.001163264,0.001385529,0.001576472,0.001362645,0.002590659,0.0018995,0.002466602,0.0008854964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001152458,"about_ca_system_score_gemma":0.001004873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009680451,"about_ca_topic_score_gemma":0.009598698,"domain_scores_codex":[0.9985128,0.0004711112,0.00009993547,0.0003987695,0.0003847968,0.0001324554],"domain_scores_gemma":[0.9969006,0.001880799,0.0001974885,0.0004286605,0.0005186542,0.00007386746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002318815,0.000103833,0.0006971245,0.0001003166,0.0002187892,0.0001348963,0.0001371762,0.6335753,0.00592798,0.04425755,0.003328494,0.3112866],"study_design_scores_gemma":[0.000004181181,0.000006814667,0.00006843814,0.000002770292,0.000008356235,0.00001573549,0.000003230951,0.9911308,0.0007501448,0.007564021,0.0004393205,0.0000061585],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.001084366,0.0001869831,0.9981939,0.00007591685,0.00002175245,0.000007135697,0.00001516295,0.0001694436,0.0002454286],"genre_scores_gemma":[0.2432065,0.0008151428,0.7503568,0.0002109805,0.000323926,0.0001057679,0.0002792379,0.0003117414,0.004389795],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.009680451,"threshold_uncertainty_score":0.01924819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05227570046109629,"score_gpt":0.3027417614590094,"score_spread":0.2504660609979131,"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."}}