{"id":"W4226049128","doi":"10.1109/tip.2022.3162961","title":"Universal Background Subtraction Based on Arithmetic Distribution Neural Network","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Histogram; Computer science; Artificial neural network; Subtraction; Artificial intelligence; Convolutional neural network; Algorithm; Probability distribution; Pattern recognition (psychology); Arithmetic; Mathematics; Image (mathematics); Statistics","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":[0.0005448415,0.0009561473,0.0008204436,0.0009582968,0.0003625425,0.0009684312,0.00164066,0.0006857566,0.001834511],"category_scores_gemma":[0.0009892654,0.0004086227,0.0006913432,0.0008980702,0.0005501261,0.001706588,0.001099047,0.001118388,0.0006022924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009816122,"about_ca_system_score_gemma":0.001006739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008575673,"about_ca_topic_score_gemma":0.00914965,"domain_scores_codex":[0.9996324,0.000037028,0.00001501918,0.0001077212,0.0001395131,0.00006833249],"domain_scores_gemma":[0.9997813,0.00005363618,0.00002508611,0.00003039701,0.00009114682,0.00001841886],"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":[0.0002623909,0.00009350517,0.00136928,0.00011802,0.0001071741,0.0001480994,0.00008843627,0.2327986,0.0381618,0.01604322,0.00271377,0.7080957],"study_design_scores_gemma":[0.000005335211,0.00001861053,0.0002526861,0.000006695039,0.00001694098,0.00005135593,0.000006272007,0.9837624,0.01193597,0.002670834,0.001263408,0.000009547156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009802951,0.0002897285,0.9866463,0.00008663007,0.00004142662,0.00002170046,0.00004801486,0.001306316,0.001756914],"genre_scores_gemma":[0.5082953,0.0008447448,0.4826941,0.0003948073,0.0000977602,0.00007307482,0.0004897053,0.0003096874,0.006800837],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008575673,"threshold_uncertainty_score":0.01705152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02486213111972385,"score_gpt":0.2792896840390449,"score_spread":0.2544275529193211,"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."}}