Research Repository

Abrupt motion tracking using a visual saliency embedded particle filter

Su, Yingya and Zhao, Qingjie and Zhao, Liujun and Gu, Dongbing (2014) 'Abrupt motion tracking using a visual saliency embedded particle filter.' Pattern Recognition, 47 (5). pp. 1826-1834. ISSN 0031-3203

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Abrupt motion is a significant challenge that commonly causes traditional tracking methods to fail. This paper presents an improved visual saliency model and integrates it to a particle filter tracker to solve this problem. Once the target is lost, our algorithm recovers tracking by detecting the target region from salient regions, which are obtained in the saliency map of current frame. In addition, to strengthen the saliency of target region, the target model is used as a prior knowledge to calculate a weight set which is utilized to construct our improved saliency map adaptively. Furthermore, we adopt the covariance descriptor as the appearance model to describe the object more accurately. Compared with several other tracking algorithms, the experimental results demonstrate that our method is more robust in dealing with various types of abrupt motion scenarios. © 2013 Elsevier Ltd. All rights reserved.

Item Type: Article
Uncontrolled Keywords: Object tracking; Abrupt motion; Particle filter; Visual saliency; Covariance descriptor
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 09 Jul 2015 15:12
Last Modified: 15 Jan 2022 00:44

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