{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T22:52:06Z","timestamp":1767567126731,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,10,10]],"date-time":"2017-10-10T00:00:00Z","timestamp":1507593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Occlusion is a challenging problem in visual tracking. Therefore, in recent years, many trackers have been explored to solve this problem, but most of them cannot track the target in real time because of the heavy computational cost. A spatio-temporal context (STC) tracker was proposed to accelerate the task by calculating context information in the Fourier domain, alleviating the performance in handling occlusion. In this paper, we take advantage of the high efficiency of the STC tracker and employ salient prior model information based on color distribution to improve the robustness. Furthermore, we exploit a scale pyramid for accurate scale estimation. In particular, a new high-confidence update strategy and a re-searching mechanism are used to avoid the model corruption and handle occlusion. Extensive experimental results demonstrate our algorithm outperforms several state-of-the-art algorithms on the OTB2015 dataset.<\/jats:p>","DOI":"10.3390\/s17102303","type":"journal-article","created":{"date-parts":[[2017,10,10]],"date-time":"2017-10-10T10:34:29Z","timestamp":1507631669000},"page":"2303","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Reliable and Real-Time Tracking Method with Color Distribution"],"prefix":"10.3390","volume":"17","author":[{"given":"Zishu","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Optoelectronics, Image Engineering & Video Technology Lab, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Yuqi","family":"Han","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Embedded Real-Time Information Processing Technique, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Tingfa","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Optoelectronics, Image Engineering & Video Technology Lab, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, Beijing 100081, China"}]},{"given":"Xiangmin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Optoelectronics, Image Engineering & Video Technology Lab, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Haiping","family":"Song","sequence":"additional","affiliation":[{"name":"China North Vehicle Research Institute, Beijing 100081 China"}]},{"given":"Jiqiang","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Optoelectronics, Image Engineering & Video Technology Lab, Beijing Institute of Technology, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, K.H., Zhang, L., Liu, Q., Zhang, D., and Yang, M.-H. 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