{"id":"W4317383733","doi":"10.1109/robio55434.2022.10011698","title":"TGRMPT: A Head-Shoulder Aided Multi-Person Tracker and a New Large-Scale Dataset for Tour-Guide Robot","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"BitTorrent tracker; Computer science; Artificial intelligence; Computer vision; Robot; Metric (unit); Tracking (education); Scale (ratio); Eye tracking; Track (disk drive); Video tracking; Visualization; Object (grammar); Engineering","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":[0.001006492,0.002713273,0.001540122,0.002002225,0.001082978,0.001057851,0.002627632,0.002307015,0.006018587],"category_scores_gemma":[0.002971472,0.000508968,0.001399766,0.002199596,0.000561346,0.001480705,0.002287726,0.001905493,0.0110581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009757176,"about_ca_system_score_gemma":0.001772733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02910739,"about_ca_topic_score_gemma":0.05863178,"domain_scores_codex":[0.9984233,0.0001960957,0.0001025549,0.0007261992,0.0003939305,0.0001578817],"domain_scores_gemma":[0.9985356,0.0001687387,0.0001367983,0.0004496742,0.0005031216,0.0002060081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001544262,0.001158361,0.01648682,0.002852489,0.0005441339,0.0007676615,0.000361156,0.0149368,0.0200785,0.001349214,0.6777598,0.2621607],"study_design_scores_gemma":[0.0008934224,0.001888011,0.1329136,0.001265095,0.00060022,0.004371784,0.001364533,0.2544981,0.04269912,0.005798602,0.553086,0.0006213459],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1033774,0.005189288,0.123568,0.0009045175,0.002166271,0.001954186,0.6740816,0.07183569,0.01692309],"genre_scores_gemma":[0.05537233,0.0004322075,0.06400302,0.0002781017,0.00009394113,0.0006322352,0.8738725,0.0007768717,0.004538877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02910739,"threshold_uncertainty_score":0.05787593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1543720609212253,"score_gpt":0.3789699168256016,"score_spread":0.2245978559043763,"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."}}