{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T09:16:37Z","timestamp":1769764597105,"version":"3.49.0"},"reference-count":22,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2012,9,7]],"date-time":"2012-09-07T00:00:00Z","timestamp":1346976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, a pixel-based background modeling method, which uses nonparametric kernel density estimation, is proposed. To reduce the burden of image storage, we modify the original KDE method by using the first frame to initialize it and update it subsequently at every frame by controlling the learning rate according to the situations. We apply an adaptive threshold method based on image changes to effectively subtract the dynamic backgrounds. The devised scheme allows the proposed method to automatically adapt to various environments and effectively extract the foreground. The method presented here exhibits good performance and is suitable for dynamic background environments. The algorithm is tested on various video sequences and compared with other state-of-the-art background subtraction methods so as to verify its performance.<\/jats:p>","DOI":"10.3390\/s120912279","type":"journal-article","created":{"date-parts":[[2012,9,7]],"date-time":"2012-09-07T11:21:14Z","timestamp":1347016874000},"page":"12279-12300","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["An Adaptive Background Subtraction Method Based on Kernel Density Estimation"],"prefix":"10.3390","volume":"12","author":[{"given":"Jeisung","family":"Lee","sequence":"first","affiliation":[{"name":"School of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-Dong, Seodaemun-Gu, Seoul 120-749, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mignon","family":"Park","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-Dong, Seodaemun-Gu, Seoul 120-749, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2012,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/34.598236","article-title":"Pfinder: Real-time tracking of the human body","volume":"19","author":"Wren","year":"1997","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_2","unstructured":"Horprasert, T., Harwood, D., and Davis, L.S. (, January September). A statistical approach for real-time robust background subtraction and shadow detection. Kerkyra, Greece."},{"key":"ref_3","unstructured":"Stauffer, C., and Grimson, W.E.L. (, January June). Adaptive background mixture models for real-time tracking. Santa Barbara, CA, USA."},{"key":"ref_4","unstructured":"KaewTraKulPong, P., and Bowden, R. (, January September). An improved adaptive background mixture model for real-time tracking with shadow detection."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1109\/TCSVT.2009.2035843","article-title":"A robust object segmentation system using a probability-based background extraction algorithm","volume":"20","author":"Chiu","year":"2010","journal-title":"IEEE Trans. Circ. Syst. Vid."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.rti.2004.12.004","article-title":"Real-time foreground-background segmentation using codebook model","volume":"11","author":"Kim","year":"2005","journal-title":"Real-Time Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1109\/TIP.2008.924285","article-title":"A self-organizing approach to background subtraction for visual surveillance applications","volume":"17","author":"Maddalena","year":"2008","journal-title":"IEEE Trans. Image Process"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1109\/JPROC.2002.801448","article-title":"Background and foreground modeling using nonparametric kernel density estimation for visual surveillance","volume":"90","author":"Elgammal","year":"2002","journal-title":"Proc. IEEE"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"127","DOI":"10.2298\/FUEE0501127I","article-title":"A fast algorithm for background tracking in video surveillance, using nonparametric kernel density estimation","volume":"18","author":"Ianasi","year":"2005","journal-title":"Facta Universitatis (NIS) Ser.: Elec. Energ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Park, J.G., and Lee, C. (2010). Bayesian rule-based complex background modeling and foreground detection. Opt. Eng.","DOI":"10.1117\/1.3319820"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1002\/int.20462","article-title":"An approach to automatic real-time novelty detection, object identification, and tracking in video streams based on recursive density estimation and evolving takagi-sugeno fuzzy systems","volume":"26","author":"Angelov","year":"2011","journal-title":"Int. J. Intell. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Reddy, V., Sanderson, C., and Lovell, B.C. (2011). A low-complexity algorithm for static background estimation from cluttered image sequences in surveillance contexts. J. Image Vide. Process.","DOI":"10.1155\/2011\/164956"},{"key":"ref_13","unstructured":"Matsuyama, T., Ohya, T., and Habe, H. (, January January). Background subtraction for nonstationary scenes. Taipei, Taiwan."},{"key":"ref_14","unstructured":"Mason, M., and Duric, Z. (, January October). Using histograms to detect and track objects in color video. Washington, DC, USA."},{"key":"ref_15","unstructured":"Monnet, A., Mittal, A., Paragious, N., and Ramesh, V. (, January October). Background Modeling and Subtraction of Dynamic Scenes. Nice, France."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2706","DOI":"10.1016\/j.patcog.2006.11.023","article-title":"Efficient hierarchical method for background subtraction","volume":"40","author":"Chen","year":"2007","journal-title":"Pattern Recog."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1109\/TCSVT.2011.2133270","article-title":"Hierarchical method for foreground detection using codebook model","volume":"21","author":"Guo","year":"2011","journal-title":"IEEE Trans. Circ. Syst. Vid."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.3390\/s100201041","article-title":"A multiscale region-based motion detection and background subtraction algorithm","volume":"10","author":"Varcheie","year":"2010","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1109\/TPAMI.2006.68","article-title":"A texture-based method for modeling the background and detecting moving objects","volume":"28","author":"Heikkila","year":"2006","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1109\/TPAMI.2003.1233909","article-title":"Detecting moving objects, ghosts, and shadows in video streams","volume":"25","author":"Cucchiara","year":"2003","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1023\/A:1009982220290","article-title":"An evaluation of statistical approaches to text categorization","volume":"1","author":"Yang","year":"1999","journal-title":"Inf. Retr."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1109\/TIP.2010.2101613","article-title":"ViBe: A Universal Background Subtraction Algorithm for Video Sequences","volume":"20","author":"Barnich","year":"2011","journal-title":"IEEE Trans. Image Process"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/12\/9\/12279\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:52:13Z","timestamp":1760219533000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/12\/9\/12279"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,9,7]]},"references-count":22,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2012,9]]}},"alternative-id":["s120912279"],"URL":"https:\/\/doi.org\/10.3390\/s120912279","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,9,7]]}}}