{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T16:22:25Z","timestamp":1788279745158,"version":"build-2803163510"},"reference-count":27,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T00:00:00Z","timestamp":1652400000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Background segmentation is a process in which an algorithm removes the static background from an image. This allows only a changing section of the image. This process is important for motion detection or object tracking. In this article, an approach is proposed to compare several existing algorithms for background segmentation under severe weather conditions. Three weather conditions were tested: falling snow, rain and a sunny windy day. The test algorithms were executed on a test video containing frames collected by a dedicated Raspberry Pi camera. The frames used in the tests included cars, bicycles, motorcycles, people, and trees. Preliminary results from these tests show interesting differences in detail detection and detection noise.<\/jats:p>","DOI":"10.7717\/peerj-cs.962","type":"journal-article","created":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T08:41:56Z","timestamp":1652431316000},"page":"e962","source":"Crossref","is-referenced-by-count":6,"title":["Background segmentation in difficult weather conditions"],"prefix":"10.7717","volume":"8","author":[{"given":"Lukasz","family":"Karbowiak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3345-604X","authenticated-orcid":true,"given":"Janusz","family":"Bobulski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2022,5,13]]},"reference":[{"issue":"6","key":"10.7717\/peerj-cs.962\/ref-1","doi-asserted-by":"publisher","first-page":"1709","DOI":"10.1109\/TIP.2010.2101613","article-title":"A universal background subtraction algorithm for video sequences","volume":"20","author":"Barnich","year":"2011","journal-title":"IEEE Transactions on Image processing"},{"key":"10.7717\/peerj-cs.962\/ref-2","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/978-3-319-23814-2_2","article-title":"2DHMM-based face recognition method","volume":"389","author":"Bobulski","year":"2016","journal-title":"Advances in Intelligent Systems and Computing"},{"issue":"4","key":"10.7717\/peerj-cs.962\/ref-3","doi-asserted-by":"publisher","first-page":"865","DOI":"10.5277\/oa120418","article-title":"Person identification system using an identikit picture of the suspect","volume":"42","author":"Bobulski","year":"2012","journal-title":"Optica Applicata"},{"key":"10.7717\/peerj-cs.962\/ref-4","article-title":"Background subtractor CNT performance","author":"Code","year":"2021"},{"key":"10.7717\/peerj-cs.962\/ref-5","first-page":"53","article-title":"Bilayer segmentation of live video","volume":"1","author":"Criminisi","year":"2006"},{"issue":"7","key":"10.7717\/peerj-cs.962\/ref-6","doi-asserted-by":"publisher","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":"Proceedings of the IEEE"},{"key":"10.7717\/peerj-cs.962\/ref-7","article-title":"Background subtractor CNT","author":"GitHub","year":"2021"},{"key":"10.7717\/peerj-cs.962\/ref-8","first-page":"4305","article-title":"Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation","author":"Godbehere","year":"2012"},{"key":"10.7717\/peerj-cs.962\/ref-9","first-page":"1159","article-title":"Background subtraction using local SVD binary pattern","author":"Guo","year":"2016"},{"issue":"5","key":"10.7717\/peerj-cs.962\/ref-10","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1109\/TPAMI.2011.243","article-title":"Density-based multifeature background subtraction with support vector machine","volume":"34","author":"Han","year":"2012","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"4","key":"10.7717\/peerj-cs.962\/ref-11","doi-asserted-by":"publisher","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 Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"10.7717\/peerj-cs.962\/ref-12","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/BF01420982","article-title":"Computing occluding and transparent motions","volume":"12","author":"Irani","year":"1994","journal-title":"International Journal of Computer Vision"},{"key":"10.7717\/peerj-cs.962\/ref-13","first-page":"656","volume-title":"Supervoxel-consistent foreground propagation in video","author":"Jain","year":"2014"},{"key":"10.7717\/peerj-cs.962\/ref-14","first-page":"135","article-title":"An improved adaptive background mixture model for real-time tracking with shadow detection","author":"KaewTraKulPong","year":"2002"},{"issue":"7","key":"10.7717\/peerj-cs.962\/ref-15","doi-asserted-by":"publisher","first-page":"1168","DOI":"10.1109\/TIP.2008.924285","article-title":"Self-organizing approach to background subtraction for visual surveillance applications","volume":"17","author":"Maddalena","year":"2008","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.7717\/peerj-cs.962\/ref-16","article-title":"Default values of algorithms","author":"LearnOpenCv","year":"2021a"},{"key":"10.7717\/peerj-cs.962\/ref-17","article-title":"Background subtractor GSOC","author":"LearnOpenCv","year":"2021b"},{"key":"10.7717\/peerj-cs.962\/ref-18","article-title":"Default values of algorithms","author":"LearnOpenCv","year":"2021c"},{"key":"10.7717\/peerj-cs.962\/ref-19","article-title":"Morphological transformations","author":"Python","year":"2021"},{"issue":"8","key":"10.7717\/peerj-cs.962\/ref-20","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1109\/34.868677","article-title":"Learning patterns of activity using real-time tracking","volume":"22","author":"Stauffer","year":"2000","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.7717\/peerj-cs.962\/ref-21","first-page":"291","article-title":"Benchmark dataset for outdoor foreground\/background extraction","author":"Vacavant","year":"2013"},{"key":"10.7717\/peerj-cs.962\/ref-22","first-page":"387","article-title":"CDnet 2014: an expanded change detection benchmark dataset","author":"Wang","year":"2014"},{"issue":"7","key":"10.7717\/peerj-cs.962\/ref-23","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1109\/34.598236","article-title":"Real-time tracking of the human body","volume":"19","author":"Wren","year":"1997","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"4","key":"10.7717\/peerj-cs.962\/ref-24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3391743","article-title":"Video object segmentation and tracking: a survey","volume":"11","author":"Yao","year":"2020","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"10.7717\/peerj-cs.962\/ref-25","article-title":"Background subtractor CNT default settings","author":"Zeevi","year":"2021"},{"key":"10.7717\/peerj-cs.962\/ref-26","first-page":"28","article-title":"Improved adaptive gaussian mixture model for background subtraction","volume":"2","author":"Zivkovic","year":"2004"},{"issue":"7","key":"10.7717\/peerj-cs.962\/ref-27","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1016\/j.patrec.2005.11.005","article-title":"Efficient adaptive density estimation per image pixel for the task of background subtraction","volume":"27","author":"Zivkovic","year":"2006","journal-title":"Pattern Recognition Letters"}],"container-title":["PeerJ Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/peerj.com\/articles\/cs-962.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-962.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-962.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/articles\/cs-962.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T08:42:12Z","timestamp":1652431332000},"score":1,"resource":{"primary":{"URL":"https:\/\/peerj.com\/articles\/cs-962"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,13]]},"references-count":27,"alternative-id":["10.7717\/peerj-cs.962"],"URL":"https:\/\/doi.org\/10.7717\/peerj-cs.962","archive":["CLOCKSS","LOCKSS","Portico"],"relation":{},"ISSN":["2376-5992"],"issn-type":[{"value":"2376-5992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,13]]},"article-number":"e962"}}