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Journal of Electronic Imaging

Compressive tracking with incremental multivariate Gaussian distribution
Author(s): Dong Dong Li; Gongjian Wen; Gao Zhu; Qiaoling Zeng
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Paper 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.

Paper Details

Date Published: 21 September 2016
PDF: 11 pages
J. Electron. Imaging. 25(5) 053015 doi: 10.1117/1.JEI.25.5.053015
Published in: Journal of Electronic Imaging Volume 25, Issue 5
Show Author Affiliations
Dong Dong Li, National Univ. of Defense Technology (China)
Gongjian Wen, National Univ. of Defense Technology (China)
Gao Zhu, The Australian National Univ. (Australia)
Qiaoling Zeng, National Univ. of Defense Technology (China)


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