{"id":"W1954025331","doi":"","title":"Fusion of spatial and visual information for object tracking on iPhone","year":2013,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer vision; Computer science; Video tracking; Artificial intelligence; Tracking (education); Object (grammar); Tracking system; Matching (statistics); Motion (physics); Eye tracking; Sensor fusion; Visualization; Match moving; Computer graphics (images); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004885871,0.0003986719,0.0005342616,0.001020758,0.0002450788,0.000519864,0.0004279775,0.0005524318,0.0008887969],"category_scores_gemma":[0.001050778,0.0002154558,0.0004077379,0.0007624977,0.0001851657,0.001077137,0.0006114166,0.0002780543,0.0004934462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000244136,"about_ca_system_score_gemma":0.0003111879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001297152,"about_ca_topic_score_gemma":0.001809234,"domain_scores_codex":[0.9995945,0.00004955387,0.00002527746,0.0001012387,0.0001816128,0.00004789775],"domain_scores_gemma":[0.9997301,0.00005407182,0.00003169193,0.00005393524,0.0001148481,0.00001539892],"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":[0.0004782178,0.00007709124,0.001790494,0.000187149,0.00008931627,0.0001948103,0.0001057104,0.01507925,0.1513385,0.001461937,0.001548337,0.8276492],"study_design_scores_gemma":[0.00005292571,0.0006262617,0.01618585,0.00007880285,0.0003470505,0.001113236,0.0001649852,0.7916676,0.1702014,0.005210297,0.01426092,0.00009056125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05660823,0.001971543,0.9374453,0.0001157205,0.0001995406,0.00005273592,0.00009958402,0.0009404763,0.0025668],"genre_scores_gemma":[0.7171776,0.00138627,0.2775472,0.0002083004,0.0001372948,0.00007186961,0.0003450518,0.00006439869,0.003062053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001297152,"threshold_uncertainty_score":0.002973318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03150228118218407,"score_gpt":0.3121970779035453,"score_spread":0.2806947967213612,"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."}}