{"id":"W2115094435","doi":"10.1109/cec.2008.4631366","title":"GPU based extraction of moving objects without shadows under intensity changes","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Background subtraction; Computer science; Artificial intelligence; Computer vision; Shadow (psychology); Acceleration; Process (computing); Feature extraction; Subtraction; Component (thermodynamics); Image (mathematics); General-purpose computing on graphics processing units; Computer graphics (images); Pixel; Graphics; 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":[0.0001491209,0.0004429161,0.0006516681,0.001098035,0.0003610174,0.0008471208,0.0005940019,0.0004263745,0.001419219],"category_scores_gemma":[0.0006408454,0.000346718,0.0003832313,0.000831241,0.0002017838,0.0006588139,0.0005144925,0.0004034102,0.0006705055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003285949,"about_ca_system_score_gemma":0.0004198542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002834591,"about_ca_topic_score_gemma":0.003418359,"domain_scores_codex":[0.9998391,0.00001804833,0.000007425408,0.00002847066,0.00008228647,0.00002471862],"domain_scores_gemma":[0.9998263,0.0000347847,0.0000188878,0.00002815157,0.0000751093,0.00001690647],"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":[0.0002432741,0.00006343643,0.002023123,0.0001241393,0.000060758,0.000233939,0.000137096,0.01937124,0.3245898,0.003319681,0.002405689,0.6474279],"study_design_scores_gemma":[0.00003518661,0.00009604668,0.005430919,0.00002564866,0.00005387967,0.0008475997,0.00007807825,0.7534393,0.2197715,0.002567244,0.01760979,0.00004479323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05100372,0.0004105229,0.9431157,0.00007680149,0.00006247444,0.00003948045,0.00007834578,0.00222541,0.002987605],"genre_scores_gemma":[0.2045368,0.0004330357,0.7897366,0.00006345286,0.00003369725,0.00003770549,0.000370301,0.0002938125,0.004494728],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002834591,"threshold_uncertainty_score":0.005636215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06194272125511709,"score_gpt":0.3073836505862156,"score_spread":0.2454409293310985,"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."}}