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Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segmentation to be solved. In this paper, we propose a new fusion method that combines depth and color information for foreground segmentation based on an advanced color-based algorithm. First, a background model and a depth model are developed. Then, based on these models, we propose a new updating strategy that can eliminate ghosting and black shadows almost completely. Extensive experiments have been performed to compare the proposed algorithm with other, conventional RGB-D (Red-Green-Blue and Depth) algorithms. The experimental results suggest that our method extracts foregrounds with higher effectiveness and efficiency.<\/jats:p>","DOI":"10.3390\/s17051177","type":"journal-article","created":{"date-parts":[[2017,5,23]],"date-time":"2017-05-23T01:47:33Z","timestamp":1495504053000},"page":"1177","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2677-4421","authenticated-orcid":false,"given":"Xiaoqin","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Hohai University, Nanjing 211100, China"},{"name":"Changzhou Key Laboratory of Robotics and Intelligent Technology, Changzhou 213022, China"},{"name":"Jiangsu Key Laboratory of Special Robots, Hohai University, Changzhou 213022, China"}]},{"given":"Xiaofeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Changzhou Key Laboratory of Robotics and Intelligent Technology, Changzhou 213022, China"},{"name":"Jiangsu Key Laboratory of Special Robots, Hohai University, Changzhou 213022, China"},{"name":"College of IoT Engineering, Hohai University, Changzhou 213022, China"}]},{"given":"Aimin","family":"Jiang","sequence":"additional","affiliation":[{"name":"Changzhou Key Laboratory of Robotics and Intelligent Technology, Changzhou 213022, China"},{"name":"Jiangsu Key Laboratory of Special Robots, Hohai University, Changzhou 213022, China"},{"name":"College of IoT Engineering, Hohai University, Changzhou 213022, China"}]},{"given":"Bin","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Electronics, Communication and Physics, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Chenguang","family":"Yang","sequence":"additional","affiliation":[{"name":"Zienkiewicz Centre for Computational Engineering, Swansea University, Swansea SA1 8EN, UK"}]}],"member":"1968","published-online":{"date-parts":[[2017,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1109\/TPAMI.2011.239","article-title":"Tracking-Learning-Detection","volume":"34","author":"Kalal","year":"2012","journal-title":"IEEE Trans. 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