{"id":"W2279000668","doi":"10.1007/978-3-642-39094-4_46","title":"Multi-distributions Particle Filter for Eye Tracking Inside a Vehicle","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Particle filter; Computer vision; Artificial intelligence; Autoregressive model; Rotation (mathematics); Eye tracking; Face (sociological concept); Tracking (education); Similarity (geometry); Filter (signal processing); Image (mathematics); 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":[0.0005541077,0.000772836,0.001055917,0.0005577913,0.00051944,0.0007638052,0.0009707758,0.001541759,0.00189043],"category_scores_gemma":[0.00144775,0.0006693116,0.0009518933,0.0007943616,0.0003018329,0.0007744011,0.000907516,0.001373018,0.001046454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007127737,"about_ca_system_score_gemma":0.0009626871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01983013,"about_ca_topic_score_gemma":0.01548855,"domain_scores_codex":[0.9996947,0.00004466376,0.00001495087,0.0001137044,0.00009617599,0.00003583376],"domain_scores_gemma":[0.9996038,0.0001838701,0.00002443668,0.00003944386,0.0001315227,0.00001696281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003437685,0.00009646086,0.00139046,0.0001826294,0.0001777113,0.0001508794,0.0001650163,0.5003952,0.01803238,0.005896275,0.00497374,0.4681955],"study_design_scores_gemma":[0.000005425119,0.0000110762,0.0003057441,0.00000521034,0.000009572813,0.00001597688,0.000005195478,0.9971821,0.00119696,0.0006588978,0.0005976818,0.000006194694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00389094,0.0003729564,0.994535,0.00005216248,0.0000834444,0.00001316447,0.00003749269,0.000413672,0.0006011963],"genre_scores_gemma":[0.3705472,0.001125071,0.6142149,0.0001648275,0.0001778875,0.0001638692,0.0004211815,0.0001910689,0.01299409],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01983013,"threshold_uncertainty_score":0.03942943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03568826234325436,"score_gpt":0.2814927893074319,"score_spread":0.2458045269641775,"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."}}