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Journal of Information Science and Engineering, Vol. 32 No. 1, pp. 213-228 (January 2016)

Fast Visual Tracking using Memory Gradient Pursuit Algorithm*

1School of Information Science and Engineering
Northeastern University
Shenyang, 110004 P.R. China
National Police University of China
Shenyang, 110035 P.R. China

Sparse representation scheme is very influential in visual tracking field. These L1 trackers obtain robustness by finding the target with the minimum reconstruction error via L1 norm minimization problem. However, the high computational burden of L1 minimization and absence of effective model for appearance changes may hamper its application in real world sceneries. In this research, we present a fast and robust tracking method that exploits a fast memory gradient pursuit algorithm (FMGP) with sparse representation scheme in a Bayesian framework to accelerate the L1 minimization process. For tracking, our approach adopts a non-overlapping covariance descriptor and uses a new similarity metric with scaled unscented transform. In order to reduce the problem of drift tracking, we construct a different template dictionary including benchmark template with different scales, adaptive background templates and stable templates. We test the proposed tracking method on the challenging image sequences. Both quantitative and qualitative results demonstrate the excellent performances of the proposed algorithm compared with several state of the art tracking algorithms.

Keywords: non-overlapping covariance descriptor, fast memory gradient pursuit, L1 minimization, visual tracking, scaled unscented transform

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Received May 12, 2014; revised July 17 & September 29, 2014; accepted October 23, 2014.
Communicated by Chung-Lin Huang.
* This work was supported by National Natural Science Foundation of China (No. 61273078) and the Science research project of the Education Department of Liaoning Province (No. L2015558).