{"id":"W1910583789","doi":"10.1109/crv.2005.24","title":"Body Tracking in HumanWalk from Monocular Video Sequences","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Silhouette; Computer vision; Artificial intelligence; Computer science; Minimum bounding box; Initialization; Tracking (education); Monocular; Segmentation; Context (archaeology); Frame (networking); Optical flow; Image (mathematics); Geography","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":[0.0002396693,0.0003756149,0.0005526127,0.0009325444,0.0002678524,0.0004068148,0.0004084195,0.0003752273,0.0009144171],"category_scores_gemma":[0.0006547528,0.0002936294,0.0001944631,0.0007817693,0.0001877095,0.0004737807,0.0004245379,0.000240134,0.0005655956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002486529,"about_ca_system_score_gemma":0.0002971259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003839431,"about_ca_topic_score_gemma":0.0100211,"domain_scores_codex":[0.9998003,0.0000262613,0.000006886735,0.00007125162,0.00006591545,0.00002937532],"domain_scores_gemma":[0.9998617,0.00002735368,0.00002882354,0.00001708686,0.00004807436,0.0000168993],"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":[0.0006960099,0.000122676,0.006995261,0.000368407,0.00008810779,0.0008018041,0.0003262606,0.03192633,0.1706924,0.001235199,0.003334376,0.7834132],"study_design_scores_gemma":[0.00003993962,0.0003026515,0.05173398,0.00008246784,0.00005568628,0.001792166,0.0002886773,0.8613232,0.07534715,0.002695764,0.006297593,0.0000408525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2431072,0.001294896,0.7503959,0.00008702966,0.0001163227,0.0001077709,0.0004765586,0.001591072,0.002823252],"genre_scores_gemma":[0.659009,0.0008672675,0.3335846,0.00009844312,0.00005116991,0.00006980616,0.001110603,0.000145629,0.005063481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003839431,"threshold_uncertainty_score":0.007634163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03263537499838576,"score_gpt":0.3160554522827022,"score_spread":0.2834200772843164,"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."}}