{"id":"W2558671692","doi":"10.1109/iemcon.2016.7746290","title":"Implementation of levels-of-detail in Bayesian tracking framework using single RGB-D sensor","year":2016,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer vision; Artificial intelligence; Particle filter; Minimum bounding box; Computer science; Tracking (education); Geodesic; Synchronizing; Tracking system; RGB color model; Feature (linguistics); Filter (signal processing); Mathematics; Image (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":[0.0006101432,0.0005783491,0.0006828717,0.0005817969,0.0004597025,0.0008382917,0.001389725,0.0009307687,0.002242949],"category_scores_gemma":[0.001331431,0.0006425807,0.0009031047,0.0004798879,0.0003346635,0.001195306,0.0008518671,0.000837433,0.0007352827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008496605,"about_ca_system_score_gemma":0.001262459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01727723,"about_ca_topic_score_gemma":0.01592937,"domain_scores_codex":[0.9994367,0.00007020067,0.0000329687,0.000158252,0.000248864,0.00005296145],"domain_scores_gemma":[0.9996288,0.0001033568,0.00004357826,0.0000655045,0.0001297522,0.00002901028],"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":[0.0002278143,0.0001350613,0.003677249,0.0002562018,0.0001647732,0.0002031043,0.0002952227,0.5437005,0.03675403,0.02615961,0.002449536,0.3859769],"study_design_scores_gemma":[0.000008131074,0.00002695711,0.0004122896,0.00000722369,0.00001619029,0.00003486623,0.000007680673,0.9924514,0.003412077,0.002084196,0.001526355,0.00001268023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00242481,0.00007795388,0.9965149,0.00002828428,0.00001146437,0.00001644465,0.00002260109,0.0003861712,0.0005172442],"genre_scores_gemma":[0.2514214,0.0003703748,0.7450956,0.00007565555,0.00003541568,0.0001080271,0.0001977071,0.0001009596,0.002594906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01727723,"threshold_uncertainty_score":0.03435338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08183079706431463,"score_gpt":0.3340487075510924,"score_spread":0.2522179104867778,"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."}}