{"id":"W4387267154","doi":"10.55041/ijsrem25911","title":"Comparative study on pose estimators such as MoveNet Lighting, MoveNet Thunder, and OpenPose (MobileNet) model for Human Pose Estimation over Real-Time Feed","year":2023,"lang":"en","type":"article","venue":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Pose; Estimator; Artificial intelligence; Ambiguity; 3D pose estimation; Estimation; Task (project management); Machine learning; Engineering; Statistics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002802264,0.0001339043,0.000208466,0.001547501,0.0002831353,0.0007266324,0.0003433622,0.00003084645,0.000005481273],"category_scores_gemma":[0.00003192874,0.0001221577,0.00005010776,0.0005752009,0.00006257943,0.0005703164,0.0002202711,0.0002071151,0.00001445126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009490743,"about_ca_system_score_gemma":0.00003354682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001403986,"about_ca_topic_score_gemma":0.000004179049,"domain_scores_codex":[0.9981005,0.00008632185,0.0004213371,0.0003583718,0.000742213,0.0002912334],"domain_scores_gemma":[0.9991935,0.000179188,0.0001099469,0.0001721511,0.0002268516,0.0001183678],"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":[0.0005002374,0.001868526,0.000310777,0.0006518112,0.0005041788,0.000390632,0.008471543,0.8525071,0.03832748,0.05864167,0.009797137,0.02802887],"study_design_scores_gemma":[0.001214194,0.0005848696,0.01050113,0.0005075934,0.000009167506,0.00001533794,0.0002772336,0.9774701,0.0005942833,0.008587815,0.0001047168,0.0001335488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796311,0.00002312888,0.01889663,0.0001877209,0.0003011151,0.0006257027,0.000005971551,0.00003650045,0.000292187],"genre_scores_gemma":[0.9961573,0.00004320194,0.002958289,0.00001007547,0.00004106724,0.00004783826,0.00001129709,0.000009666946,0.0007211945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.124963,"threshold_uncertainty_score":0.7006933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08247160653907401,"score_gpt":0.3991300404164895,"score_spread":0.3166584338774154,"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."}}