{"id":"W4408062962","doi":"10.5220/0013153500003905","title":"Silhouette Segmentation for Near-Fall Detection Through Analysis of Human Movements in Surveillance Videos","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Silhouette; Segmentation; Computer vision; Artificial intelligence; Computer science; Image segmentation","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":[0.0007860398,0.0001040524,0.0002958338,0.0002939968,0.00009411901,0.00006765203,0.0003204471,0.0000517207,0.00000523843],"category_scores_gemma":[0.0000630458,0.0001021429,0.0001325973,0.002135636,0.00002477366,0.0003248301,0.0000682943,0.00005558271,8.972864e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005822016,"about_ca_system_score_gemma":0.00002695434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009927407,"about_ca_topic_score_gemma":0.007947705,"domain_scores_codex":[0.9987546,0.0001476036,0.0004016784,0.0003466462,0.0001619634,0.0001874804],"domain_scores_gemma":[0.9991103,0.0002483297,0.0001376689,0.0003786288,0.0001098551,0.00001517678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006337998,0.0002621453,0.6365553,0.000140559,0.001058141,0.000002050628,0.001623882,0.01166503,0.06653583,0.01103477,0.0001134882,0.2709455],"study_design_scores_gemma":[0.001059689,0.0001010773,0.7686595,0.00001979137,0.00003796194,1.133117e-7,0.00006762787,0.1129137,0.1072787,0.00944371,0.0002332965,0.0001847629],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3369149,0.00002239988,0.6618345,0.00009972572,0.0001269294,0.0001909778,0.000003510539,0.0000451481,0.0007618666],"genre_scores_gemma":[0.9490238,0.000007557814,0.05030796,0.0004040186,0.000008085295,0.00004658511,0.00001255562,0.000003997993,0.0001854684],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6121088,"threshold_uncertainty_score":0.4435006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02555296787024709,"score_gpt":0.3525611330323719,"score_spread":0.3270081651621248,"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."}}