{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004642287,0.0001447623,0.0001564914,0.0004039179,0.0001193213,0.0003805422,0.0003275645,0.00009057744,0.0001089604],"category_scores_gemma":[0.0002779316,0.0001265377,0.00005655075,0.0001284571,0.00003128269,0.004152718,0.0001086793,0.0001185841,0.0001203378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004447397,"about_ca_system_score_gemma":0.0000657175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001338238,"about_ca_topic_score_gemma":0.000006332593,"domain_scores_codex":[0.9986121,0.00004604486,0.0005474098,0.0001314237,0.0005114107,0.0001516226],"domain_scores_gemma":[0.9983612,0.0001907004,0.0004116473,0.0001578347,0.000821704,0.00005689842],"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":[0.0001102954,0.00004738981,0.0005924969,0.0000405262,0.00001429468,1.315239e-7,0.001276551,0.0002172558,0.002608148,0.07648055,0.000294477,0.9183179],"study_design_scores_gemma":[0.002114754,0.001026986,0.06729355,0.00029771,0.000005202791,0.000009904324,0.0003844525,0.8920153,0.02109125,0.00956588,0.005804056,0.0003909622],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2104107,0.000003027734,0.7754098,0.00149876,0.001044441,0.0005491788,0.00002396954,0.00009307567,0.01096714],"genre_scores_gemma":[0.9926099,0.00003689357,0.00653387,0.000588947,0.00005574398,0.00003718057,0.0001093951,0.000003456389,0.00002463285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9179269,"threshold_uncertainty_score":0.5160059,"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."}}