{"id":"W2913977709","doi":"10.3390/s19040750","title":"Deep Attention Models for Human Tracking Using RGBD","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"Camouflage; Artificial intelligence; Computer science; Computer vision; RGB color model; Object (grammar); Feature (linguistics); Tracking (education); Active appearance model; Video tracking; Layer (electronics); Modular design; Eye tracking; Property (philosophy); Pattern recognition (psychology); Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005312321,0.0009551944,0.0005454308,0.0006162258,0.0002847646,0.0007009793,0.001609517,0.001025758,0.002780959],"category_scores_gemma":[0.001317224,0.000493338,0.0007822679,0.0007136386,0.0004343102,0.0009753918,0.0009582384,0.001338013,0.0008666237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001780345,"about_ca_system_score_gemma":0.0007144719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0314304,"about_ca_topic_score_gemma":0.02785338,"domain_scores_codex":[0.9997947,0.00002819028,0.000008270816,0.00008261204,0.00004592364,0.00004044068],"domain_scores_gemma":[0.999764,0.00008404737,0.00003197284,0.0000296378,0.00007120657,0.00001921209],"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":[0.0001858943,0.00008544043,0.001180292,0.00006398729,0.00006067937,0.00006212848,0.00007856862,0.8296397,0.007285606,0.004973711,0.002506584,0.1538775],"study_design_scores_gemma":[0.000001791244,0.000008515821,0.0001686413,0.000002614605,0.000004688705,0.000005214049,0.000001322062,0.9979491,0.0004636746,0.001207647,0.0001845174,0.000002319471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0393917,0.001452653,0.9518909,0.0004367091,0.0001454334,0.0000387489,0.0003009368,0.002924377,0.003418504],"genre_scores_gemma":[0.8977961,0.0008364696,0.09146192,0.0003579695,0.00009363125,0.0001019191,0.0004828568,0.0001298723,0.008739319],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0314304,"threshold_uncertainty_score":0.06249487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07473545932203214,"score_gpt":0.3354438198992686,"score_spread":0.2607083605772365,"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."}}