{"id":"W4254562804","doi":"10.32920/ryerson.14665893","title":"SoC for real - time object tracking in 3D space","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Alertness; Object (grammar); Computer science; Tracking (education); Video tracking; Warning system; Space (punctuation); Hazardous waste; Work (physics); Simulation; Human–computer interaction; Computer vision; Computer security; Real-time computing; Aeronautics; Artificial intelligence; Engineering; 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":[0.0004093577,0.0006770939,0.0004454729,0.0005152224,0.0002060312,0.0009320909,0.001022759,0.0007865379,0.007680434],"category_scores_gemma":[0.0009913802,0.0002716776,0.0005063776,0.0003256009,0.0002561512,0.0007057774,0.000821166,0.0004568564,0.002320392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002779464,"about_ca_system_score_gemma":0.0005218417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001976582,"about_ca_topic_score_gemma":0.002386781,"domain_scores_codex":[0.9995481,0.00004390595,0.00002352787,0.00006760352,0.0002576776,0.00005911044],"domain_scores_gemma":[0.9995025,0.0001078729,0.0000601992,0.0001345958,0.000155048,0.00003974927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008940298,0.0003291302,0.002958657,0.0009642809,0.0002422543,0.0009544829,0.000457796,0.07620011,0.2944434,0.01449888,0.02111821,0.5869387],"study_design_scores_gemma":[0.0001572246,0.001275362,0.003658719,0.0001466371,0.0001351262,0.00122764,0.0001012066,0.8000072,0.1081639,0.006412214,0.07859882,0.0001159801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02490694,0.0004376433,0.9503052,0.00009759083,0.0002162091,0.0001814262,0.0003095056,0.01537682,0.008168701],"genre_scores_gemma":[0.6081305,0.000656187,0.3730026,0.0003994151,0.00009454879,0.0005221115,0.0009077424,0.000507954,0.01577904],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007680434,"threshold_uncertainty_score":0.02569366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04381131404775677,"score_gpt":0.3398763593293146,"score_spread":0.2960650452815579,"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."}}