Optimized Compressive Tracking in Co-training Framework

ZHENG Chao; CHEN Jie; YIN Songfeng; YANG Xing; FENG Yunsong; LING Yongshun

doi:10.11999/JEIT151001

Volume 38 Issue 7

Jul. 2016

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(7): 1624-1630

ZHENG Chao, CHEN Jie, YIN Songfeng, YANG Xing, FENG Yunsong, LING Yongshun. Optimized Compressive Tracking in Co-training Framework[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1624-1630. doi: 10.11999/JEIT151001

Citation:

ZHENG Chao, CHEN Jie, YIN Songfeng, YANG Xing, FENG Yunsong, LING Yongshun. Optimized Compressive Tracking in Co-training Framework[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1624-1630. doi: 10.11999/JEIT151001

Citation:

PDF( 20800 KB)

Optimized Compressive Tracking in Co-training Framework

doi: 10.11999/JEIT151001

1.
(State Key Laboratory of Pulsed Power Laser Technology, Electronic Engineering Institute, Hefei 230037, China)
2.
(Electronics and Information Engineering Institute, Anhui Jianzhu University, Hefei 230037, China)

Funds:

Higher Education Institutes Natural Science Research Project of Anhui Province of China (KJ2015ZD14), The National Natural Science Foundation of China (61405248, 61503394), The Natural Science Foundation of Anhui Province (1408085QF131, 1508085QF121)

Received Date: 2015-09-08
Rev Recd Date: 2016-01-11
Publish Date: 2016-07-19

Abstract

Abstract

As visual tracking algorithms based on traditional co-training framework are characterized by poor robustness in complex environment, an optimized compressive tracking algorithm in a novel co-training criterion is proposed. Firstly, the spatial layout information and the online feature selection technique based on maximizing entropy energy are used to improve the discriminative capacity of compressive sense classifier, and two independent classifiers are constructed by structural compressive features selected from the gray intensity space and the local binary pattern space respectively. Secondly, on the basis of the classifiers collaborative strategy that is acquired by calculating the confidence score distribution entropy of the candidate samples, complementary features can be adaptive fused, which reinforces the robustness of tracking results. Thirdly, as assistant of the cascaded Histograms of Orientation Gradient (HOG) classifier, the collaborative appearance model is updated with accuracy by a novel co-training criterion with sample selecting ability, which solves the updating error of co-training accumulation problem. Comparative experiment results on extensive challenging sequences demonstrate that the proposed algorithm is of better performance than other similar tracking algorithms.
- Visual tracking,
- Co-training,
- Spatial layout information,
- Compressive sense classifier,
- Entropy

FullText(HTML)

References(23)

References

LI Xi, HU Weiming, SHEN Chunhua, et al. A survey of appearance models in visual object tracking[J]. ACM Transactions on Intelligent Systems and Technology, 2013, 4(4): 58. doi: 10.1145/2508037.2508039.

袁广林, 薛模根. 基于稀疏稠密结构表示与在线鲁棒字典学习的视觉跟踪[J]. 电子与信息学报, 2015, 37(3): 536-542. doi: 10.11999/JEIT140507.

YUAN Guanglin and XUE Mogen. Visual tracking based on sparse dense structure representation and online robust dictionary learning[J]. Journal of Electronics Information Technology, 2015, 37(3): 536-542. doi: 10.11999/JEIT140507.

HU Hongwei, MA Bo, and JIA Yunde. Multi-task l0 gradient minimization for visual tracking[J]. Neurocomputing, 2015, 54(1): 41-49. doi: 10.1016/j.neucom.2014.12.021.

HU Weiming, LI Wei, ZHANG Xiaoqin, et al. Single and multiple object tracking using a multi-feature joint sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(4): 816-833. doi: 10.1109/ TPAMI.2014.2353628.

YIN Yingjie, XU De, WANG Xingang, et al. Online state-based structured SVM combined with incremental PCA for robust visual tracking[J]. IEEE Transactions on Cybernetics, 2015, 45(9): 1988-2000. doi: 10.1109/TCYB. 2014.2363078.

KIM D H, KIM H K, LEE S J, et al. Kernel-based structural binary pattern tracking[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(8): 1288-1300. doi: 10.1109/TCSVT.2014.2305514.

陈思, 苏松志, 李绍滋, 等. 基于在线半监督 boosting 的协同训练目标跟踪算法[J]. 电子与信息学报, 2014, 36(4): 888-895. doi: 10.3724/SP.J.1146.2013.00826.

CHEN Si, SU Songzhi, LI Shaozi, et al. A novel co-training object tracking algorithm based on online semi-supervised boosting[J]. Journal of Electronics Information Technology, 2014, 36(4): 888-895. doi: 10.3724/SP.J.1146.2013.00826.

BLUM A and MITCHELL T. Combining labeled and unlabeled data with co-training[C]. Proceedings of ACM 11th Annual Conference on Computational Learning Theory, USA, 1998: 92-100.

TANG F, BRENNAN S, ZHAO Q, et al. Co-tracking using semi-supervised support vector machines[C]. IEEE International Conference on Computer Vision, Brazil, 2007: 1-8.

YU Q, DINH T B, and MEDIONI G. Online tracking and reacquisition using co-trained generative and discriminative trackers[C]. European Conference on Computer Vision, Springer, Berlin Heidelberg, 2008: 678-691.

LIU Rong, CHENG Jian, and LU Hanqing. A robust boosting tracker with minimum error bound in a co-training framework[C]. IEEE International Conference on Computer Vision, Japan, 2009: 1459-1466.

BABENKO B, YANG M H, and BELONGIE S. Visual tracking with online multiple instance learning[C]. IEEE Conference on Computer Vision and Pattern Recognition, USA, 2009: 983-990.

LU Huchuan, ZHOU Qiuhong, WANG Dong, et al. A co-training framework for visual tracking with multiple instance learning[C]. IEEE International Conference on Automatic Face Gesture Recognition and Workshops, Spain, 2011: 539-544.

ZHANG Kaihua, ZHANG Lei, and YANG M H. Fast compressive tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(10): 2002-2015. doi: 10.1109/TPAMI.2014.2315808.

ZHU Jianzhang, MA Yue, QIN Qianqing, et al. Adaptive weighted real-time compressive tracking[J]. IET Computer Vision, 2014, 8(6): 740-752. doi: 10.1049/iet-cvi.2013.0255.

DALAL N and TRIGGS B. Histograms of oriented gradients for human detection[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, USA, 2005: 886-893.

HEIKKILA M and PIETIKAINEN M. A texture-based method for modeling the background and detecting moving objects[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4): 657-662. doi: 10.1109/ TPAMI.2006.68.

ZHOU Zhihua and LI Ming. Tri-training: exploiting unlabeled data using three classifiers[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(11): 1529-1541. doi: 10.1109/TKDE.2005.186.

WU Yi, LIM J, and YANG M H. Online object tracking: A benchmark[C]. IEEE Conference on Computer Vision and Pattern Recognition, USA, 2013: 2411-2418.

ZHANG Kaihua, ZHANG Lei, and YANG M H. Real-time object tracking via online discriminative feature selection [J]. IEEE Transactions on Image Processing, 2013, 22(12): 4664-4677. doi: 10.1109/TIP.2013.2277800.

ZHANG Kaihua and SONG Huihui. Real-time visual tracking via online weighted multiple instance learning[J]. Pattern Recognition, 2013, 46(1): 397-411. doi: 10.1016/ j.patcog.2012.07.013.

Relative Articles

Supplements(0)

Cited By

Proportional views