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Sensors 2017, 17(4), 666; doi:10.3390/s17040666

Multi-View Structural Local Subspace Tracking

1
Image Engineering&Video Technology Lab, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China
2
Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Received: 20 December 2016 / Revised: 18 March 2017 / Accepted: 21 March 2017 / Published: 23 March 2017
(This article belongs to the Section Physical Sensors)
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Abstract

In this paper, we propose a multi-view structural local subspace tracking algorithm based on sparse representation. We approximate the optimal state from three views: (1) the template view; (2) the PCA (principal component analysis) basis view; and (3) the target candidate view. Then we propose a unified objective function to integrate these three view problems together. The proposed model not only exploits the intrinsic relationship among target candidates and their local patches, but also takes advantages of both sparse representation and incremental subspace learning. The optimization problem can be well solved by the customized APG (accelerated proximal gradient) methods together with an iteration manner. Then, we propose an alignment-weighting average method to obtain the optimal state of the target. Furthermore, an occlusion detection strategy is proposed to accurately update the model. Both qualitative and quantitative evaluations demonstrate that our tracker outperforms the state-of-the-art trackers in a wide range of tracking scenarios. View Full-Text
Keywords: visual tracking; sparse representation; structural local appearance model; multi-view; PCA visual tracking; sparse representation; structural local appearance model; multi-view; PCA
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Guo, J.; Xu, T.; Shi, G.; Rao, Z.; Li, X. Multi-View Structural Local Subspace Tracking. Sensors 2017, 17, 666.

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