{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:42:12Z","timestamp":1760240532767,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental\u2002Research\u2002Funds\u2002for\u2002the\u2002Central\u2002Universities","doi-asserted-by":"publisher","award":["20103196833"],"award-info":[{"award-number":["20103196833"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Excellent performance, real-time and low memory requirement are three vital requirements for target detection in high resolution marine radar system. Unfortunately, many current state-of-the-art methods merely achieve excellent performance when coping with highly complex scenes. In fact, a common problem is that real-time processing, low memory requirement and remarkable detection ability are difficult to coordinate. To address this issue, we propose a novel detection framework which bases its principle on sampling and spatiotemporal detection. The framework consists of two stages, coarse detection and fine detection. Sampling-based coarse detection is designed to guarantee the real-time processing and low memory requirements by locating the area where targets may exist in advance. Different from former detection methods, multi-scan video data are utilized. In the stage of fine detection, the candidate areas are grouped into three categories: single target, dense targets and sea clutter. Different approaches for processing the different categories are implemented to achieve excellent performance. The superiority of the proposed framework beyond state-of-the-art baselines is well substantiated in this work. Low memory requirement of the proposed framework was verified by theoretical analysis. Real-time processing capability was verified by the video data of two real scenarios. Synthetic data were tested to show the improvement in tracking performance by using the proposed detection framework.<\/jats:p>","DOI":"10.3390\/s19132912","type":"journal-article","created":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T10:54:47Z","timestamp":1561978487000},"page":"2912","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Efficient Extended Targets Detection Framework Based on Sampling and Spatio-Temporal Detection"],"prefix":"10.3390","volume":"19","author":[{"given":"Bo","family":"Yan","sequence":"first","affiliation":[{"name":"School of Aerospace Science and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xi\u2019an 710126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Life Sciences and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xi\u2019an 710126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbo","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xi\u2019an 710126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muqing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xi\u2019an 710126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luping","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xi\u2019an 710126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3268","DOI":"10.1109\/TAES.2012.6324703","article-title":"Extended Target Tracking Using a Gaussian-Mixture PHD Filter","volume":"48","author":"Granstrom","year":"2012","journal-title":"IEEE Trans. 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