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Compressive tracking with incremental multivariate Gaussian distribution

[+] Author Affiliations
Dongdong Li, Gongjian Wen, Qiaoling Zeng

National University of Defense Technology, College of Electronic Science and Engineering, Yanwachi 137, Kaifu District, Changsha, Hunan 410073, China

Gao Zhu

Australian National University, Research School of Engineering, Canberra, Acton, ACT 2601, Australia

J. Electron. Imaging. 25(5), 053015 (Sep 21, 2016). doi:10.1117/1.JEI.25.5.053015
History: Received April 3, 2016; Accepted August 30, 2016
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Abstract.  Various approaches have been proposed for robust visual tracking, among which compressive tracking (CT) yields promising performance. In CT, Haar-like features are efficiently extracted with a very sparse measurement matrix and modeled as an online updated naïve Bayes classifier to account for target appearance change. The naïve Bayes classifier ignores overlap between Haar-like features and assumes that Haar-like features are independently distributed, which leads to drift in complex scenario. To address this problem, we present an extended CT algorithm, which assumes that all Haar-like features are correlated with each other and have multivariate Gaussian distribution. The mean vector and covariance matrix of multivariate normal distribution are incrementally updated with constant computational complexity to adapt to target appearance change. Each frame is associated with a temporal weight to expend less modeling power on old observation. Based on temporal weight, an update scheme with changing but convergent learning rate is derived with strict mathematic proof. Compared with CT, our extended algorithm achieves a richer representation of target appearance. The incremental multivariate Gaussian distribution is integrated into the particle filter framework to achieve better tracking performance. Extensive experiments on the CVPR2013 tracking benchmark demonstrate that our proposed tracker achieves superior performance both qualitatively and quantitatively over several state-of-the-art trackers.

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© 2016 SPIE and IS&T

Citation

Dongdong Li ; Gongjian Wen ; Gao Zhu and Qiaoling Zeng
"Compressive tracking with incremental multivariate Gaussian distribution", J. Electron. Imaging. 25(5), 053015 (Sep 21, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.5.053015


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