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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2023,9,30]]},"abstract":"<jats:p>\n            Moving object detection is still a challenging task in complex scenes. The existing methods based on deep learning mainly use U-Nets and have achieved amazing results. However, they ignore the local continuity between pixels. In order to solve this problem, a method based on a superpixel fusion network (SF-Net) is proposed in this article. First, the median filter is used to extract the candidate foreground (called\n            <jats:italic>pixel features<\/jats:italic>\n            ) and the image sequence is segmented by superpixel. Then, the histogram features (called\n            <jats:italic>superpixel features<\/jats:italic>\n            ) of the candidate foreground superpixels are extracted. Next, the pixel features and the superpixel features are the inputs of SF-Net, respectively. Experiments show the effectiveness of SF-Net on 34 image sequences and the average F-measure reaches 0.84. SF-Net can remove more background noise and has stronger expression ability than a network with the same depth.\n          <\/jats:p>","DOI":"10.1145\/3579998","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T15:58:54Z","timestamp":1673539134000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Detection of Moving Object Using Superpixel Fusion Network"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0087-3472","authenticated-orcid":false,"given":"Yang","family":"Li","sequence":"first","affiliation":[{"name":"School of IoT Engineering (School of Information Security), Jiangsu Vocational College of Information Technology, Wuxi, China"}]}],"member":"320","published-online":{"date-parts":[[2023,3,16]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2018.8451603"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2101613"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.11.004"},{"issue":"3","key":"e_1_3_1_5_2","first-page":"147","article-title":"Recent advanced statistical background modeling for foreground detection: A systematic survey","volume":"4","author":"Bouwmans T.","year":"2011","unstructured":"T. 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