{"id":"W2909077096","doi":"10.1109/oceans.2018.8604597","title":"Synthetically Trained 3D Visual Tracker of Underwater Vehicles","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Orientation (vector space); Convolutional neural network; Offset (computer science); Object detection; Minimum bounding box; Robot; Pose; Eye tracking; Image plane; Bounding overwatch; Underwater; Image (mathematics); Pattern recognition (psychology)","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":[0.0003634589,0.0006453909,0.0003807698,0.0006185262,0.000183478,0.0005757422,0.0008659381,0.000735438,0.001608708],"category_scores_gemma":[0.001775468,0.0004897649,0.0005336527,0.0003932215,0.0004798577,0.0003997245,0.0007633152,0.0005601326,0.0005513991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007782943,"about_ca_system_score_gemma":0.000661092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009509937,"about_ca_topic_score_gemma":0.009413292,"domain_scores_codex":[0.9997326,0.00003965473,0.00001006173,0.00008294319,0.0001005577,0.0000342956],"domain_scores_gemma":[0.9995827,0.0001197405,0.00005110731,0.00007069691,0.0001269324,0.00004881854],"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":[0.0001356399,0.00007926836,0.002176361,0.00008877432,0.00004062734,0.0001575888,0.00006410349,0.9391187,0.01713041,0.001137642,0.001523259,0.03834751],"study_design_scores_gemma":[0.000007366624,0.00002995825,0.0007675239,0.000005915377,0.000003345894,0.00002983468,0.00001023517,0.9936744,0.004690567,0.0002460353,0.0005283837,0.000006352795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4117487,0.0002962616,0.5710676,0.0002894787,0.000348746,0.0002315339,0.001869722,0.006032599,0.008115271],"genre_scores_gemma":[0.8917147,0.0001217528,0.1027191,0.0000749258,0.00002082057,0.0001134033,0.002391587,0.0001979155,0.002645665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009509937,"threshold_uncertainty_score":0.01890922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008835777243222416,"score_gpt":0.2204415404253906,"score_spread":0.2116057631821682,"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."}}