{"id":"W2124798821","doi":"10.1007/s11760-008-0055-6","title":"Feature-based detection and correction of occlusions and split of video objects","year":2008,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; Communications Research Centre Canada","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Feature (linguistics); Segmentation; Tracking (education); Object detection; Video tracking; Superposition principle; Occlusion; Pattern recognition (psychology); Object (grammar); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006613765,0.0006583893,0.001084553,0.001406555,0.0004649608,0.0007745764,0.0008226756,0.0006727227,0.001371282],"category_scores_gemma":[0.002491383,0.0004200868,0.0005164662,0.001214377,0.0003462264,0.0009886463,0.000754539,0.000797363,0.0006491177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003362786,"about_ca_system_score_gemma":0.0006616697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00174562,"about_ca_topic_score_gemma":0.002657513,"domain_scores_codex":[0.9994134,0.00005356564,0.00002551695,0.0001535457,0.0002663381,0.00008766173],"domain_scores_gemma":[0.9987212,0.0002465826,0.0001646914,0.000292723,0.000512542,0.00006224105],"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":[0.0006935287,0.0001509935,0.005431536,0.0001175158,0.00008851341,0.0001779341,0.0001786157,0.01118721,0.271007,0.001663897,0.00199619,0.707307],"study_design_scores_gemma":[0.00004482231,0.000249913,0.03272754,0.00002987827,0.0001916822,0.001233381,0.00008927576,0.676711,0.2796284,0.002414245,0.006625231,0.00005470335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07154457,0.0003226244,0.9258371,0.00004728035,0.00007551908,0.00003298171,0.0001261526,0.001279374,0.0007344077],"genre_scores_gemma":[0.4785617,0.0003851477,0.5174056,0.00005658657,0.00007209984,0.0000622915,0.0008106998,0.0003526745,0.002293132],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00174562,"threshold_uncertainty_score":0.004587412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01670772890452617,"score_gpt":0.2703217018152611,"score_spread":0.2536139729107349,"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."}}