{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T10:09:29Z","timestamp":1778753369664,"version":"3.51.4"},"reference-count":56,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,3]],"date-time":"2018-07-03T00:00:00Z","timestamp":1530576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>In the context of video background\u2013foreground separation, we propose a compressive online Robust Principal Component Analysis (RPCA) with optical flow that separates recursively a sequence of video frames into foreground (sparse) and background (low-rank) components. This separation method operates on a small set of measurements taken per frame, in contrast to conventional batch-based RPCA, which processes the full data. The proposed method also leverages multiple prior information by incorporating previously separated background and foreground frames in an n-\u21131 minimization problem. Moreover, optical flow is utilized to estimate motions between the previous foreground frames and then compensate the motions to achieve higher quality prior foregrounds for improving the separation. Our method is tested on several video sequences in different scenarios for online background\u2013foreground separation given compressive measurements. The visual and quantitative results show that the proposed method outperforms other existing methods.<\/jats:p>","DOI":"10.3390\/jimaging4070090","type":"journal-article","created":{"date-parts":[[2018,7,3]],"date-time":"2018-07-03T11:12:58Z","timestamp":1530616378000},"page":"90","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Compressive Online Video Background\u2013Foreground Separation Using Multiple Prior Information and Optical Flow"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0857-2690","authenticated-orcid":false,"given":"Srivatsa","family":"Prativadibhayankaram","sequence":"first","affiliation":[{"name":"Multimedia Communications and Signal Processing, University of Erlangen-Nuremberg, 91058 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4562-9406","authenticated-orcid":false,"given":"Huynh Van","family":"Luong","sequence":"additional","affiliation":[{"name":"Multimedia Communications and Signal Processing, University of Erlangen-Nuremberg, 91058 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thanh Ha","family":"Le","sequence":"additional","affiliation":[{"name":"Human Machine Interaction, University of Engineering and Technology, Vietnam National University, Hanoi 100000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9","family":"Kaup","sequence":"additional","affiliation":[{"name":"Multimedia Communications and Signal Processing, University of Erlangen-Nuremberg, 91058 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,3]]},"reference":[{"key":"ref_1","first-page":"11","article-title":"Robust principal component analysis?","volume":"58","author":"Li","year":"2011","journal-title":"JACM"},{"key":"ref_2","unstructured":"Wright, J., Ganesh, A., Rao, S., Peng, Y., and Ma, Y. 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