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Sensors 2017, 17(12), 2889; doi:10.3390/s17122889

Consistently Sampled Correlation Filters with Space Anisotropic Regularization for Visual Tracking

1
School of Optoelectronics, Image Engineering & Video Technology Lab, 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: 1 November 2017 / Revised: 4 December 2017 / Accepted: 11 December 2017 / Published: 12 December 2017
(This article belongs to the Section Physical Sensors)
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Abstract

Most existing correlation filter-based tracking algorithms, which use fixed patches and cyclic shifts as training and detection measures, assume that the training samples are reliable and ignore the inconsistencies between training samples and detection samples. We propose to construct and study a consistently sampled correlation filter with space anisotropic regularization (CSSAR) to solve these two problems simultaneously. Our approach constructs a spatiotemporally consistent sample strategy to alleviate the redundancies in training samples caused by the cyclical shifts, eliminate the inconsistencies between training samples and detection samples, and introduce space anisotropic regularization to constrain the correlation filter for alleviating drift caused by occlusion. Moreover, an optimization strategy based on the Gauss-Seidel method was developed for obtaining robust and efficient online learning. Both qualitative and quantitative evaluations demonstrate that our tracker outperforms state-of-the-art trackers in object tracking benchmarks (OTBs). View Full-Text
Keywords: correlation filter; online learning; sample consistency; visual tracking correlation filter; online learning; sample consistency; visual tracking
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Shi, G.; Xu, T.; Guo, J.; Luo, J.; Li, Y. Consistently Sampled Correlation Filters with Space Anisotropic Regularization for Visual Tracking. Sensors 2017, 17, 2889.

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