Dual-kernel Tracking Approach Based on Second-order Spatiogram

Zhang Can-long; Tang Yan-ping; Li Zhi-xin; Ma Hai-fei; Cai Bing

doi:10.11999/JEIT141321

Volume 37 Issue 7

Jul. 2015

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Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(7): 1660-1666

Zhang Can-long, Tang Yan-ping, Li Zhi-xin, Ma Hai-fei, Cai Bing. Dual-kernel Tracking Approach Based on Second-order Spatiogram[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1660-1666. doi: 10.11999/JEIT141321

Citation:

Zhang Can-long, Tang Yan-ping, Li Zhi-xin, Ma Hai-fei, Cai Bing. Dual-kernel Tracking Approach Based on Second-order Spatiogram[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1660-1666. doi: 10.11999/JEIT141321

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Dual-kernel Tracking Approach Based on Second-order Spatiogram

doi: 10.11999/JEIT141321

1.
2.
(Guangxi Experiment Center of Information Science, Guilin University of Electronic Technology, Guilin 541004, China)

Received Date: 2014-10-15
Rev Recd Date: 2015-01-06
Publish Date: 2015-07-19

Abstract

Abstract

In order to avoid the loss of background and spatial information in mean shift tracker, a dual-kernel tracking approach based on the second-order spatiogram is proposed. In the method, the second-order spatiogram is employed to represent a target, the similarity and contrast are considered simultaneously when evaluating the target candidate, and they are adaptively integrated into a novel objective function. By performing multi-variable Taylor series expansion and maximization on the objective function, a dual-kernel target location-shift formula is induced. Finally, the optimal target location is gained recursively by applying the mean shift procedure. Experimental evaluations on several image sequences demonstrate the effectiveness of the proposed algorithm.
- Pattern recognition,
- Dual-kernel tracking,
- Spatiogram,
- Jensen-Shannon divergence

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References(20)

References

Zhang S, Yao H, Sun X, et al.. Sparse coding based visual tracking: review and experimental comparison[J]. Pattern Recognition, 2013, 46(7): 1772-1788.

Wu Y, Lim J, and Yang M. Online object tracking: a benchmark[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Oregon, Portland, USA, 2013: 2411-2418.

Isard M and Black A. Condensation-conditional density propagation for visual tracking[J]. International Journal on Computer Vision, 1998, 29(1): 5-28.

程旭, 李拟珺, 周同池, 等. 稀疏表示的超像素在线跟踪[J]. 电子与信息学报, 2014, 36(10): 2393-2399.

Cheng Xu, Li Ni-jun, Zhou Tong-chi, et al.. Online tracking via superpixel and sparse representation[J]. Journal of Electronics Information Technology, 2014, 36(10): 2393-2399.

Comaniciu D, Ramesh V, and Meer P. Kernel-based object tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-577.

Leichter I. Mean shift trackers with cross-bin metrics[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(4): 695-706.

Avidan S. Support vector tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(8): 1064-1072.

Zhang K and Song H. Real-time visual tracking via online weighted multiple instance learning[J]. Pattern Recognition, 2013, 46(1): 397-411.

Ross D, Lim J, Lin R, et al.. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(3): 125-141.

Mei X and Ling H. Robust visual tracking using l1 minimization[C]. Proceedings of the International Conference on Computer Vision, Kyoto, Japan, 2009: 1436-1443.

Fouad B, Lynda D, and Hichem S. Improved mean shift integrating texture and color features for robust real time object tracking[J]. The Visual Computer, 2013, 29(3): 155-170.

Tomas V, Jana N, and Jiri M. Robust scale-adaptive mean- shift for tracking[J]. Lecture Notes in Computer Science, 2013 (7944): 652-663.

Birchfield S and Rangarajan S. Spatiograms versus histograms for region-based tracking[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, SanDiego, California, USA, 2005: 1158-1163.

Collins R, Liu Y, and Leordeanum M. Online selection of discriminative tracking features[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1631-1643.

Ning J, Zhang L, Zhang D, et al.. Robust mean shift tracking with corrected background-weighted histogram[J]. IET Computer Vision, 2012, 6(1): 62-69.

Conaire C, O'Connor N, and Smeaton A. An improved spatiogram similarity measure for robust object localization [C]. Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Hawaii, USA, 2007: 1069-1072.

Lin J. Divergence measures based on the shannon entropy[J]. IEEE Transactions on Information Theory, 1991, 37(1): 145-151.

Liu Y, Tong S, and Chen C. Adaptive fuzzy control via observer design for uncertain nonlinear systems with unmodeled dynamics[J]. IEEE Transactions on Fuzzy Systems, 2013, 21(2): 275-288.

Zhang C, Jing Z, and Jin B. A dual-kernel-based tracking approach for visual target[J]. SCIENCE CHINA: Information Sciences, 2012, 55(3): 566-576.

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