{"id":"W2177776506","doi":"10.1016/j.procs.2015.10.030","title":"Video Foreground Detection in Non-static Background Using Multi-dimensional Color Space","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Foreground detection; Computer graphics (images); Space (punctuation); Color space; Background subtraction; Pixel; Image (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":[0.0006215983,0.0007799192,0.000624113,0.001183685,0.0003155969,0.0008888415,0.0007171134,0.0006172936,0.0006700637],"category_scores_gemma":[0.001366692,0.0001730091,0.0003715053,0.0008956383,0.0003646977,0.0008125331,0.0004458039,0.0004240197,0.000286549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003852911,"about_ca_system_score_gemma":0.0004045893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002917977,"about_ca_topic_score_gemma":0.003010631,"domain_scores_codex":[0.9995918,0.00006916438,0.00001923547,0.0001234948,0.0001310196,0.00006527433],"domain_scores_gemma":[0.9994173,0.0001750848,0.00006780592,0.00007690521,0.0002015857,0.00006137531],"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":[0.001373206,0.0005236454,0.01062746,0.0003470929,0.0001808813,0.0004420437,0.0002705421,0.07506799,0.3384576,0.003124283,0.001423528,0.5681617],"study_design_scores_gemma":[0.00003896248,0.0003982141,0.01085613,0.00001769844,0.00006146153,0.000530393,0.00009522073,0.820092,0.1656135,0.0007473283,0.001508029,0.00004108255],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.355776,0.0006702449,0.6390395,0.00009987953,0.00008847831,0.0001014057,0.0001247935,0.002317105,0.001782586],"genre_scores_gemma":[0.6332162,0.0004340774,0.3647106,0.00007949061,0.00002634168,0.00003532777,0.0002864277,0.00007645229,0.001135084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002917977,"threshold_uncertainty_score":0.005802035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0785705464570195,"score_gpt":0.3329366249137926,"score_spread":0.2543660784567731,"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."}}