{"id":"W4317877728","doi":"10.1109/irc55401.2022.00045","title":"An Improved Approach to 6D Object Pose Tracking in Fast Motion Scenarios","year":2022,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"BitTorrent tracker; Pose; Artificial intelligence; Computer vision; Computer science; Tracking (education); Video tracking; Extended Kalman filter; Motion estimation; Motion (physics); Baseline (sea); Object detection; Robot; Object (grammar); Kalman filter; Eye tracking; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001794576,0.00008117729,0.00008488537,0.0001699229,0.0000821031,0.00004284665,0.0000953189,0.00002413588,0.0002445715],"category_scores_gemma":[0.000008342807,0.00009146597,0.00002254766,0.0002962898,0.000002168427,0.0001513916,0.00002452878,0.0002242819,0.0000128888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001109216,"about_ca_system_score_gemma":0.000004778026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008885151,"about_ca_topic_score_gemma":0.00003896112,"domain_scores_codex":[0.9994015,0.00003556257,0.0001408249,0.0001515899,0.0001033041,0.0001672382],"domain_scores_gemma":[0.9997903,0.000007354304,0.00001126836,0.0001293984,0.000008496751,0.00005324042],"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":[0.000004097415,0.00003645689,0.001042184,0.000008596281,0.000002641787,0.000001250561,0.001897946,0.976663,0.009835627,0.0001750179,0.00003209669,0.01030107],"study_design_scores_gemma":[0.0001905678,0.00003114622,0.02297292,0.00000181894,0.000001465442,0.000005186512,0.001553389,0.9747,0.000187215,0.000008571309,0.0002262194,0.0001215277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5557827,0.00002039989,0.4135331,0.00005960252,0.0003298452,0.000431117,6.270081e-7,0.0007111487,0.02913138],"genre_scores_gemma":[0.9966027,2.853751e-7,0.002854883,0.00009446809,0.00004653094,0.00003561813,0.00002375428,0.00002633787,0.0003154252],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4408199,"threshold_uncertainty_score":0.3729874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01974106082688597,"score_gpt":0.2389964662843828,"score_spread":0.2192554054574968,"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."}}