{"id":"W2136806931","doi":"10.1109/crv.2012.33","title":"Robust Background Subtraction Using Geodesic Active Contours in ICA Subspace for Video Surveillance Applications","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Solink (Canada); Institut National de la Recherche Scientifique; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Background subtraction; Artificial intelligence; Computer science; Subspace topology; Computer vision; Foreground detection; Geodesic; Subtraction; Background image; Process (computing); Image subtraction; Pattern recognition (psychology); Robustness (evolution); Change detection; Image (mathematics); Image processing; Pixel; Mathematics; Binary image","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001591799,0.0001817656,0.0002639178,0.0001493946,0.0001757705,0.0001263867,0.0004051391,0.00009020304,0.00001137431],"category_scores_gemma":[0.00007663725,0.0001790828,0.00008723787,0.0006305,0.00004402019,0.001387472,0.00006351496,0.0001613044,0.00001831099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001957253,"about_ca_system_score_gemma":0.00008551873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003480044,"about_ca_topic_score_gemma":0.0006981295,"domain_scores_codex":[0.998315,0.0002071631,0.0002768535,0.0004233805,0.0001899531,0.000587642],"domain_scores_gemma":[0.9981433,0.0008945982,0.0001702525,0.0005221026,0.0001445497,0.000125178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002688391,0.001438675,0.4939292,0.0002287475,0.0002009352,0.000007113058,0.002209403,0.01721653,0.02104943,0.2031307,0.00118233,0.2591381],"study_design_scores_gemma":[0.002349718,0.000112014,0.8130246,0.00005174295,0.00002283634,0.00008406225,0.0007023494,0.1389253,0.01160162,0.005715708,0.02605245,0.001357577],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07204881,0.0001882312,0.9251877,0.0003411444,0.0003601021,0.0005943374,0.00000562085,0.0001397984,0.001134232],"genre_scores_gemma":[0.7641058,0.00001315952,0.2352891,0.0001622925,0.000190349,0.0001385638,0.000005406834,0.00001563569,0.00007967838],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.692057,"threshold_uncertainty_score":0.7302783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1286840858362531,"score_gpt":0.350465346501661,"score_spread":0.2217812606654079,"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."}}