{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T15:01:21Z","timestamp":1770908481182,"version":"3.50.1"},"reference-count":31,"publisher":"Cambridge University Press (CUP)","issue":"10","license":[{"start":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T00:00:00Z","timestamp":1614038400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:title>SUMMARY<\/jats:title><jats:p>Visual tracking is an essential building block for target tracking and capture of the underwater vehicles. On the basis of remotely autonomous control architecture, this paper has proposed an improved kernelized correlation filter (KCF) tracker and a novel fuzzy controller. The model is trained to learn an online correlation filter from a plenty of positive and negative training samples. In order to overcome the influence from occlusion, the improved KCF tracker has been designed with an added self-discrimination mechanism based on system confidence uncertainty. The novel fuzzy logic tracking controller can automatically generate and optimize fuzzy rules. Through Q-learning algorithm, the fuzzy rules are acquired through the estimating value of each state action pairs. An S surface based fitness function has been designed for the improvement of learning based particle swarm optimization. Tank and channel experiments have been carried out to verify the proposed tracker and controller through pipe tracking and target grasp on the basis of designed open frame underwater vehicle.<\/jats:p>","DOI":"10.1017\/s0263574720001502","type":"journal-article","created":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T05:23:32Z","timestamp":1614057812000},"page":"1791-1805","source":"Crossref","is-referenced-by-count":11,"title":["Intelligent Target Visual Tracking and Control Strategy for Open Frame Underwater Vehicles"],"prefix":"10.1017","volume":"39","author":[{"given":"Chaoyu","family":"Sun","sequence":"first","affiliation":[]},{"given":"Zhaoliang","family":"Wan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4551-8957","authenticated-orcid":false,"given":"Hai","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Guocheng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xuan","family":"Bao","sequence":"additional","affiliation":[]},{"given":"Jiyong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Mingwei","family":"Sheng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0553-4581","authenticated-orcid":false,"given":"Xu","family":"Yang","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2021,2,23]]},"reference":[{"key":"S0263574720001502_ref12","doi-asserted-by":"crossref","unstructured":"[12] Wang, L. , \u201cStct: Sequentially Training Convolutional Networks for Visual Tracking,\u201d Proceedings of Conference on Computer Vision and Pattern Recognition (2016) pp. 1373\u20131381.","DOI":"10.1109\/CVPR.2016.153"},{"key":"S0263574720001502_ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2017.08.025"},{"key":"S0263574720001502_ref13","doi-asserted-by":"crossref","unstructured":"[13] Wang, L. , \u201cVisual Tracking with Fully Convolutional Networks,\u201d Proceedings of International Conference on Computer Vision (2015) pp. 3119\u20133127.","DOI":"10.1109\/ICCV.2015.357"},{"key":"S0263574720001502_ref14","first-page":"188","article-title":"Stability control for the head of a biomimetic robotic fish with embedded vision","volume":"37","author":"Sun","year":"2015","journal-title":"Robot."},{"key":"S0263574720001502_ref1","doi-asserted-by":"publisher","DOI":"10.1631\/jzus.C1300171"},{"key":"S0263574720001502_ref26","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21554"},{"key":"S0263574720001502_ref28","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574709005499"},{"key":"S0263574720001502_ref30","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.neucom.2019.01.084","article-title":"Faster R-CNN for marine organisms detection and recognition using data augmentation","volume":"337","author":"Hai","year":"2019","journal-title":"Neurocomputing"},{"key":"S0263574720001502_ref16","doi-asserted-by":"crossref","unstructured":"[16] Henriques, J. , \u201cExploiting the Circulant Structure of Tracking-by-Detection with Kernels,\u201d Proceedings of 12th European Conference on Computer Vision (2012) pp. 702\u2013715.","DOI":"10.1007\/978-3-642-33765-9_50"},{"key":"S0263574720001502_ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2345390"},{"key":"S0263574720001502_ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2016.2628803"},{"key":"S0263574720001502_ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2013.01.029"},{"key":"S0263574720001502_ref17","doi-asserted-by":"crossref","unstructured":"[17] Huang, R. 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