{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:37:11Z","timestamp":1760240231700,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,4,4]],"date-time":"2019-04-04T00:00:00Z","timestamp":1554336000000},"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>In the field of visual tracking, discriminative correlation filter (DCF)-based trackers have made remarkable achievements with their high computational efficiency. The crucial challenge that still remains is how to construct qualified samples without boundary effects and redetect occluded targets. In this paper a feature-enhanced discriminative correlation filter (FEDCF) tracker is proposed, which utilizes the color statistical model to strengthen the texture features (like the histograms of oriented gradient of HOG) and uses the spatial-prior function to suppress the boundary effects. Then, improved correlation filters using the enhanced features are built, the optimal functions of which can be effectively solved by Gauss\u2013Seidel iteration. In addition, the average peak-response difference (APRD) is proposed to reflect the degree of target-occlusion according to the target response, and an adaptive Kalman filter is established to support the target redetection. The proposed tracker achieved a success plot performance of 67.8% with 5.1 fps on the standard datasets OTB2013.<\/jats:p>","DOI":"10.3390\/s19071625","type":"journal-article","created":{"date-parts":[[2019,4,5]],"date-time":"2019-04-05T11:36:01Z","timestamp":1554464161000},"page":"1625","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improved Correlation Filter Tracking with Enhanced Features and Adaptive Kalman Filter"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6498-8917","authenticated-orcid":false,"given":"Hao","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Arms and Control Engineering, Army Academy of Armored Forces, Beijing 100072, China"}]},{"given":"Yingqing","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Arms and Control Engineering, Army Academy of Armored Forces, Beijing 100072, China"}]},{"given":"Zhihong","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Arms and Control Engineering, Army Academy of Armored Forces, Beijing 100072, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1109\/CVPR.2005.177","article-title":"Histograms of oriented gradients for human detection","volume":"Volume 1","author":"Schmid","year":"2005","journal-title":"Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1145\/1177352.1177355","article-title":"Object tracking: A survey","volume":"38","author":"Yilmaz","year":"2006","journal-title":"ACM Comput. 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