{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:11:32Z","timestamp":1760238692984,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,8,21]],"date-time":"2020-08-21T00:00:00Z","timestamp":1597968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Intelligent surveillance systems enable secured visibility features in the smart city era. One of the major models for pre-processing in intelligent surveillance systems is known as saliency detection, which provides facilities for multiple tasks such as object detection, object segmentation, video coding, image re-targeting, image-quality assessment, and image compression. Traditional models focus on improving detection accuracy at the cost of high complexity. However, these models are computationally expensive for real-world systems. To cope with this issue, we propose a fast-motion saliency method for surveillance systems under various background conditions. Our method is derived from streaming dynamic mode decomposition (s-DMD), which is a powerful tool in data science. First, DMD computes a set of modes in a streaming manner to derive spatial\u2013temporal features, and a raw saliency map is generated from the sparse reconstruction process. Second, the final saliency map is refined using a difference-of-Gaussians filter in the frequency domain. The effectiveness of the proposed method is validated on a standard benchmark dataset. The experimental results show that the proposed method achieves competitive accuracy with lower complexity than state-of-the-art methods, which satisfies requirements in real-time applications.<\/jats:p>","DOI":"10.3390\/sym12091397","type":"journal-article","created":{"date-parts":[[2020,8,23]],"date-time":"2020-08-23T21:28:06Z","timestamp":1598218086000},"page":"1397","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Motion Saliency Detection for Surveillance Systems Using Streaming Dynamic Mode Decomposition"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6539-2725","authenticated-orcid":false,"given":"Thien-Thu","family":"Ngo","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Kyung Hee University Global Campus, Yongin 17104, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3940-3929","authenticated-orcid":false,"given":"VanDung","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kyung Hee University Global Campus, Yongin 17104, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3684-2923","authenticated-orcid":false,"given":"Xuan-Qui","family":"Pham","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kyung Hee University Global Campus, Yongin 17104, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6865-6650","authenticated-orcid":false,"given":"Md-Alamgir","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kyung Hee University Global Campus, Yongin 17104, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0184-6975","authenticated-orcid":false,"given":"Eui-Nam","family":"Huh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kyung Hee University Global Campus, Yongin 17104, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,21]]},"reference":[{"key":"ref_1","first-page":"353367","article-title":"Learning to detect a salient object","volume":"33","author":"Liu","year":"2011","journal-title":"IEEE Trans. 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