Target Tracking Based on Enhanced Flock of Tracker and Deep Learning

Cheng Shuai; Cao Yong-gang; Sun Jun-xi; Zhao Li-rong; Liu Guang-wen; Han Guang-liang

doi:10.11999/JEIT141362

Volume 37 Issue 7

Jul. 2015

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Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(7): 1646-1653

Cheng Shuai, Cao Yong-gang, Sun Jun-xi, Zhao Li-rong, Liu Guang-wen, Han Guang-liang. Target Tracking Based on Enhanced Flock of Tracker and Deep Learning[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1646-1653. doi: 10.11999/JEIT141362

Citation:

Cheng Shuai, Cao Yong-gang, Sun Jun-xi, Zhao Li-rong, Liu Guang-wen, Han Guang-liang. Target Tracking Based on Enhanced Flock of Tracker and Deep Learning[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1646-1653. doi: 10.11999/JEIT141362

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Target Tracking Based on Enhanced Flock of Tracker and Deep Learning

doi: 10.11999/JEIT141362

1.
(School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China)
2.

Received Date: 2014-10-29
Rev Recd Date: 2015-03-23
Publish Date: 2015-07-19

Abstract

Abstract

To solve the problem that the tracking algorithm often leads to drift and failure based on the appearance model and traditional machine learning, a tracking algorithm is proposed based on the enhanced Flock of Tracker (FoT) and deep learning under the Tracking-Learning-Detection (TLD) framework. The target is predicted and tracked by the FoT, the cascaded predictor is added to improve the precision of the local tracker based on the spatio-temporal context, and the global motion model is evaluated by the speed-up random sample consensus algorithm to improve the accuracy. A deep detector is composed of the stacked denoising autoencoder and Support Vector Machine (SVM), combines with a multi-scale scanning window with global search strategy to detect the possible targets. Each sample is weighted by the weighted P-N learning to improve the precision of the deep detector. Compared with the state-of-the-art trackers, according to the results of experiments on variant challenging image sequences in the complex environment, the proposed algorithm has more accuracy and better robust, especially for the occlusions, the background clutter and so on.
- Computer vision,
- Flock of Tracker (FoT),
- Tracking-Learning-Detection (TLD),
- Deep learning,
- Support Vector Machine (SVM),
- Deep detector

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References(29)

References

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

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

Babenko B, Yang M H, and Belongie S. Robust object tracking with online multiple instance learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619-1632.

陈东成, 朱明, 高文, 等. 在线加权多示例学习实时目标跟踪[J]. 光学精密工程, 2014, 22(6): 1661-1667.

Chen Dong-cheng, Zhu Ming, Gao Wen, et al.. Real-time object tracking via online weighted multiple instance learning [J]. Optics and Precision Engineerin, 2014, 22(6): 1661-1667.

He S F, Yang Q X, Rynson L, et al.. Visual Tracking via Locality Sensitive Histograms[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 2427-2434.

Grabner H, Grabner M, and Bischof H. Real-time tracking via online boosting[C]. Proceedings of British Machine Vision Conference, Edinburgh, UK, 2006: 47-56.

Grabner H, Leistner C, and Bischof H. Semi-supervised on-line boosting for robust tracking[C]. Proceedings of European Conference on Computer Vision, Berlin, Germany, 2008: 234-247.

颜佳, 吴敏渊. 遮挡环境下采用在线Boosting的目标跟踪[J]. 光学精密工程, 2012, 20(2): 439-446.

Yan Jia and Wu Ming-yuan. On-line boosting based target tracking under occlusion[J]. Optics and Precision Engineering, 2012, 20(2): 439-446.

Kalal Z, Matas J, and Mikolajczyk K. P-N learning: bootstrapping binary classifiers by structural constraints[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, New York, USA, 2010: 49-56.

郑胤, 陈权崎, 章毓晋. 深度学习及其在目标和行为识别中的新进展[J]. 中国图像图形学报, 2014, 19(2): 175-184.

Zheng Ying, Chen Quan-qi, and Zhang Yu-jin. Deep learning and its new progress in object and behavior recognition[J]. Journal of Image and Graphic, 2014, 19(2): 175-184.

Tomas V and Jiri M. Robustifying the flock of trackers[C]. Proceedings of Computer Vision Winter Workshop, Graz, Austria, 2011: 91-97.

周鑫, 钱秋朦, 叶永强, 等. 改进后的TLD视频目标跟踪方法[J]. 中国图象图形学报, 2013, 18(9): 1115-1123.

Zhou Xin, Qian Qiu-meng, Ye Yong-qiang, et al.. Improved TLD visual target tracking algorithm[J]. Journal of Image and Graphic, 2013, 18(9): 1115-1123.

Kalal Z, Mikolajczyk K, and Matas J. Tracking-learning- detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422.

Zhang K, Zhang L, Liu Q, et al.. Fast visual tracking via dense spatio-temporal context learning[C]. Proceedings of European Conference on Computer Vision, Zurich, Switzerland, 2014: 127-141.

Botterill T, Mills S, and Green R D. New conditional sampling strategies for speeded-up RANSAC[C]. Proceedings of British Machine Vision Conference, London, UK, 2009: 1-11.

Vincent P, Larochelle H, Lajoie I, et al.. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11(2): 3371-3408.

Tang Yi-chuan. Deep learning using linear support vector machines[C]. Proceedings of International Conference on Machine Learning: Challenges in Representational Learning Workshop, Atlanta, USA, 2013: 266-272.

Hinton G E and Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.

Torralba A, Fergus R, and Freeman W T. 80 million tiny images: a large data set for nonparametric object and scene recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(11): 1958-1970.

高文, 汤洋, 朱明. 复杂背景下目标检测的级联分类器算法研究[J]. 物理学报, 2014, 63(9): 094204.

Gao Wen, Tang Yang, and Zhu Ming. Study on the cascade classifier in target detection under complex background[J]. Acta Physica Sinica, 2014, 63(9): 094204.

Collins R T, Zhou X H, and Teh S K. An open source tracking test bed and evaluation web site[C]. Proceedings of IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, Breckenridge, USA, 2005: 17-24.

Stalder S, Grabner H, and Van G L. Beyond semi-supervised tracking: tracking should be as simple as detection, but not simpler than recognition[C]. Proceedings of IEEE Conference on Computer Vision Workshops, Kyoto, Japan, 2009: 1409-1416.

Dinh T B, Vo N, and Medion G. Context tracker: exploring supporters and distracters in unconstrained environments[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2011: 1177-1184.

Qian Yu, Thang B D, and Gerard M. Online tracking and reacquisition using co-trained generative and discriminative trackers[C]. Proceedings of European Conference on Computer Vision, Marseille, France, 2008: 678-691.

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