{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005537939,0.0001075321,0.0001600336,0.00008646326,0.0001408357,0.0001229214,0.0002911956,0.00006002533,0.000004827903],"category_scores_gemma":[0.00001968072,0.0001065934,0.0001139659,0.0001704943,0.00001380402,0.000411864,0.00004938474,0.00007718008,0.00002165255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003170952,"about_ca_system_score_gemma":0.00001454305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002778492,"about_ca_topic_score_gemma":0.00001197965,"domain_scores_codex":[0.9989569,0.00008372583,0.0001822199,0.0003400227,0.0001605542,0.0002765302],"domain_scores_gemma":[0.9992877,0.0001016128,0.00008784112,0.0003921778,0.00008922756,0.00004146436],"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":[0.00002167965,0.0001094344,0.01661262,0.0001811963,0.00009175335,0.00001576546,0.002027053,0.6008766,0.09272891,0.1639123,0.00003552166,0.1233872],"study_design_scores_gemma":[0.0003203558,0.00003802401,0.003596118,0.00002639647,0.000005406344,0.00000976,0.0000326813,0.9689204,0.001894152,0.02484523,0.0001418123,0.0001696359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4792385,0.00002110207,0.5196764,0.00004079431,0.0002908852,0.000129946,4.541033e-7,0.00007722547,0.0005247244],"genre_scores_gemma":[0.8535319,0.000001455312,0.1461299,0.00006651758,0.00007740993,0.000002817864,0.000002115648,0.00001340705,0.0001744348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3742934,"threshold_uncertainty_score":0.4346751,"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."}}