{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:04:45Z","timestamp":1760241885875,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,10,24]],"date-time":"2018-10-24T00:00:00Z","timestamp":1540339200000},"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>Although tracking research has achieved excellent performance in mathematical angles, it is still meaningful to analyze tracking problems from multiple perspectives. This motivation not only promotes the independence of tracking research but also increases the flexibility of practical applications. This paper presents a significant tracking framework based on the multi-dimensional state\u2013action space reinforcement learning, termed as multi-angle analysis collaboration tracking (MACT). MACT is comprised of a basic tracking framework and a strategic framework which assists the former. Especially, the strategic framework is extensible and currently includes feature selection strategy (FSS) and movement trend strategy (MTS). These strategies are abstracted from the multi-angle analysis of tracking problems (observer\u2019s attention and object\u2019s motion). The content of the analysis corresponds to the specific actions in the multidimensional action space. Concretely, the tracker, regarded as an agent, is trained with Q-learning algorithm and    \u03f5   -greedy exploration strategy, where we adopt a customized rewarding function to encourage robust object tracking. Numerous contrast experimental evaluations on the OTB50 benchmark demonstrate the effectiveness of the strategies and improvement in speed and accuracy of MACT tracker.<\/jats:p>","DOI":"10.3390\/s18113606","type":"journal-article","created":{"date-parts":[[2018,10,24]],"date-time":"2018-10-24T10:40:48Z","timestamp":1540377648000},"page":"3606","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Adaptive Object Tracking via Multi-Angle Analysis Collaboration"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6031-9334","authenticated-orcid":false,"given":"Wanli","family":"Xue","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Tianjin University, Tianjin 300350, China"}]},{"given":"Zhiyong","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer Software, Tianjin University, Tianjin 300350, China"},{"name":"Shenzhen Research Institute of Tianjin University, Shenzhen 518000, China"}]},{"given":"Chao","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Software, Tianjin University, Tianjin 300350, China"}]},{"given":"Zhaopeng","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Computer Software, Tianjin University, Tianjin 300350, China"}]},{"given":"Chengwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Information Science and Technology College, Dalian Maritime University, Dalian 116026, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lim, J., and Yang, M.H. 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