{"id":"W2165482536","doi":"10.1109/imtc.2011.5944102","title":"Visual object tracking based on filtering methods","year":2011,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Particle filter; Tracking (education); Computer vision; Computer science; Artificial intelligence; Object (grammar); Video tracking; Eye tracking; Filter (signal processing); Constraint (computer-aided design); Task (project management); Auxiliary particle filter; Mathematics; Kalman filter; Engineering; Extended Kalman filter; Ensemble Kalman filter","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":[0.00118708,0.0006197131,0.0009183642,0.0009164591,0.0003025188,0.0006681408,0.0007221898,0.0008123438,0.0009327413],"category_scores_gemma":[0.002367055,0.0002885619,0.000693622,0.0008566648,0.000479014,0.001052005,0.0004538783,0.0005676805,0.0003775852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005587304,"about_ca_system_score_gemma":0.000630494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003931781,"about_ca_topic_score_gemma":0.002379019,"domain_scores_codex":[0.9991964,0.0001535911,0.00005078745,0.0001749217,0.0003707411,0.00005358154],"domain_scores_gemma":[0.9991618,0.0004267204,0.00007571384,0.00009305575,0.0002150091,0.00002766829],"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":[0.000244764,0.00009016781,0.001055967,0.00025966,0.0001232915,0.00009671583,0.0001247668,0.2969022,0.04568998,0.02207463,0.001484442,0.6318535],"study_design_scores_gemma":[0.00001235278,0.00003896005,0.0003536505,0.00000687628,0.00001328733,0.00004358718,0.000003745156,0.9884396,0.006582088,0.002911396,0.001582936,0.00001155619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002476026,0.0001454022,0.9967272,0.00001859561,0.00002651203,0.0000129147,0.000005899825,0.0001718013,0.0004155034],"genre_scores_gemma":[0.3070958,0.001142064,0.6881959,0.0001097009,0.0001290116,0.0001289585,0.0001367247,0.00008589381,0.002975922],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003931781,"threshold_uncertainty_score":0.007817805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0706832334205116,"score_gpt":0.3334511387994302,"score_spread":0.2627679053789186,"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."}}