{"id":"W2067140628","doi":"10.1117/12.704537","title":"Occlusion and split detection and correction for object tracking in surveillance applications","year":2007,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Communications Research Centre Canada","funders":"","keywords":"Artificial intelligence; Computer vision; Occlusion; Computer science; Segmentation; Tracking (education); Feature (linguistics); Object detection; Object (grammar); Video tracking; Pattern recognition (psychology)","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":[0.00090011,0.0007230655,0.0008279036,0.001434834,0.0006262339,0.0008325204,0.001183641,0.0007612489,0.001031525],"category_scores_gemma":[0.002291095,0.0004166899,0.0005586245,0.0009059358,0.0005016169,0.001148351,0.0009248124,0.0007572706,0.0006830394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005589707,"about_ca_system_score_gemma":0.0008034628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002262871,"about_ca_topic_score_gemma":0.002446124,"domain_scores_codex":[0.9989647,0.00009345356,0.0000643044,0.0002535002,0.0005243582,0.00009971227],"domain_scores_gemma":[0.999017,0.0002577549,0.0002033129,0.0001726331,0.0003012345,0.00004816848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005023205,0.0001305344,0.005076294,0.0001072063,0.00007121039,0.0002321816,0.000250177,0.02440367,0.09204446,0.002963465,0.002395724,0.8718228],"study_design_scores_gemma":[0.00003157773,0.000248967,0.008513939,0.0000331451,0.00008038273,0.000797837,0.00006572583,0.8891248,0.08874876,0.002630722,0.009676998,0.00004724155],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02058574,0.0003140872,0.9773196,0.00003029102,0.00003984004,0.00005277666,0.00003079718,0.001084734,0.0005422577],"genre_scores_gemma":[0.2980264,0.0004333655,0.6987433,0.0000742456,0.00006694264,0.0001022488,0.0003506022,0.0001769266,0.002026009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002262871,"threshold_uncertainty_score":0.004760265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01229185791984563,"score_gpt":0.2622637493603064,"score_spread":0.2499718914404608,"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."}}