An Easily Initialized Visual Tracking Algorithm Based on Similar Structure for Convolutional Neural Network

LI Huanyu; BI Duyan; ZHA Yufei; YANG Yuan

doi:10.11999/JEIT150600

Volume 38 Issue 1

Jan. 2016

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LI Huanyu, BI Duyan, ZHA Yufei, YANG Yuan. An Easily Initialized Visual Tracking Algorithm Based on Similar Structure for Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2016, 38(1): 1-7. doi: 10.11999/JEIT150600

Citation:

LI Huanyu, BI Duyan, ZHA Yufei, YANG Yuan. An Easily Initialized Visual Tracking Algorithm Based on Similar Structure for Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2016, 38(1): 1-7. doi: 10.11999/JEIT150600

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An Easily Initialized Visual Tracking Algorithm Based on Similar Structure for Convolutional Neural Network

doi: 10.11999/JEIT150600

1.
2.
(College of ATC Navigation, Air Force Engineering University, Xi&rsquo

Funds:

The National Natural Science Foundation of China (61202339, 61472442), Aeronautical Science Foundation of China (20131996013)

Received Date: 2015-05-21
Rev Recd Date: 2015-08-28
Publish Date: 2016-01-19

Abstract

Abstract

On the issues about the robustness in visual object tracking, based on Principal Component Analysis (PCA) and Convolutional Neural Network (CNN), a novel visual tracking algorithm with deep feature, which is acquired from a easily initialized CNN structure, is proposed. First, the original image is processed by affine transformation. Next, layered PCA learning is used to process the normalized size image, the eigenvectors learned by PCA are used to be the filters of a CNN structure to realize initialization. Then, the deep expression of the object is extracted by this CNN structure. Finally, combining particle filter algorithm, a simple logistic regression classifier is used to realize target tracking. The result shows that the deep feature acquired from the easily initialized CNN structure has a better expressivity, it can distinguish the object and background effectively. The proposed algorithm has a better inflexibility to illumination, occlusion, rotation and camera shake, and it exhibits a good robustness and accuracy in many video sequences.
- Visual tracking,
- Deep learning,
- Feature extraction,
- Convolutional Neural Network (CNN),
- Principal Component Analysis (PCA),
- Affine transformation

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References(25)

References

LI X, HU W M, and SHEN C H. A survey of appearance models in visual object tracking[J]. ACM Transactions on Intelligent Systems and Technology, 2013, 4(4): 5801-5848.

侯志强, 黄安奇, 余旺盛, 等. 基于局部分块和模型更新的视觉跟踪算法[J]. 电子与信息学报, 2015, 37(6): 1357-1364. doi: 10.11999/JEIT141134.

HOU Zhiqiang, HUANG Anqi, YU Wangsheng, et al. Visual object tracking method based on local patch model and model update[J]. Journal of Electronics Information Technology, 2015, 37(6): 1357-1364. doi: 10.11999/ JEIT141134.

李寰宇, 毕笃彦, 杨源, 等. 基于深度特征表达与学习的视觉跟踪算法研究[J]. 电子与信息学报, 2015, 37(9): 2033-2039. doi: 10.11999/JEIT150031.

LI Huanyu, BI Duyan, YANG Yuan, et al. Research on visual tracking algorithm based on deep feature expression and learning[J]. Journal of Electronics Information Technology, 2015, 37(9): 2033-2039. doi: 10.11999/JEIT150031.

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

SUN Y, WANG X, and TANG X. Deep learning face representation from predicting 10,000 classes[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014: 1891-1898.

ABDEL-HAMID O, MOHAMED A R, JIANG H, et al. Convolutional neural networks for speech recognition[J].ACM Transactions on Audio, Speech, and Language Processing, 2014, 22(10): 1533-1545.

OUYANG W, CHU X, and WANG X. Multi-source deep learning for human pose estimation[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014: 2337-2344.

EVGENY A S, DENIS M T, and SERGE N A. Comparison of regularization methods for imagenet classification with deep convolutional neural networks[J]. AASRI Procedia, 2014, 6(8): 89-94.

ZHOU S S, CHEN Q C, and WANG X L. Convolutional deep networks for visual data classification[J]. Neural Processing Letters, 2013, 38(11): 17-27.

WANG N Y and YEUNG D Y. Learning a deep compact image representation for visual tracking[C]. Advances in Neural Information Processing Systems, Lake Tahoe, 2013: 125-137.

LI H X, LI Y, and FATIH P. Deep track: learning discriminative feature representations by convolutional neural networks for visual tracking[C]. Proceedings of the British Machine Vision Conference, Nottingham, 2014: 110-119.

BALDI P and HORNIK K. Neural networks and principal component analysis: learning from examples without local minima[J]. Neural Networks, 1989, 2(1): 53-58.

P?EREZ P, HUE C, and VERMAAK J. Color-based probabilistic tracking[C]. European Conference on Computer Vision, Copenhagen, 2002: 661-675.

ZHANG K H, ZHANG L, and YANG M H. Real-time compressive tracking[C]. European Conference on Computer Vision, Florence, 2012: 864-877.

SEVILLA-LARA L and LEARNED-MILLER E. Distribution fields for tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Colorado, 2011: 1910-1917.

ADAM A, RIVLIN E, and SHIMSHONI I. Robust fragments-based tracking using the integral histogram[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Colorado, 2006: 798-805.

ROSS D, LIM Jongwoo, and LIN Rueisung. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1): 125-141.

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

SHAUL O, AHARON B H, and DAN L. Locally orderless tracking[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Rhode Island, 2012: 1940-1947.

LIU Baiyang, HUANG Junzhou, and YANG Lin. Robust tracking using local sparse appearance model and K-selection[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Colorado, 2011: 1313-1320.

HARE S, SAFFARI A, and TORR P H S. Struck: Structured output tracking with kernels[C]. Proceedings of IEEE International Conference on Computer Vision, Colorado, 2011: 263-270.

JUNSEOK K and KYOUNG M. Tracking by sampling trackers[C]. Proceedings of IEEE International Conference on Computer Vision, Colorado, 2011: 1195-1202.

EVERINGHAM M, VAN GODL L, WILLIAMS C, et al. The pascal Visual Object Classes (VOC) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303-338.

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