Research on Visual Tracking Algorithm Based on Deep Feature Expression and Learning

Li Huan-yu; Bi Du-yan; Yang Yuan; Zha Yu-fei; Qin Bing; Zhang Li-chao

doi:10.11999/JEIT150031

Volume 37 Issue 9

Sep. 2015

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Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(9): 2033-2039

Li Huan-yu, Bi Du-yan, Yang Yuan, Zha Yu-fei, Qin Bing, Zhang Li-chao. 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

Citation:

Li Huan-yu, Bi Du-yan, Yang Yuan, Zha Yu-fei, Qin Bing, Zhang Li-chao. 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

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Research on Visual Tracking Algorithm Based on Deep Feature Expression and Learning

doi: 10.11999/JEIT150031

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

Received Date: 2015-01-06
Rev Recd Date: 2015-04-28
Publish Date: 2015-09-19

Abstract

Abstract

For the robustness of visual object tracking, a new tracking algorithm based on multi-stage convolution filtering feature is proposed by introducing deep learning into visual tracking. The algorithm uses the Principal Component Analysis (PCA) eigenvectors obtained by stratified learning, to extract the deeper abstract expression of the original image by multi-stage convolutional filtering. Then the Bhattacharyya distance is used to evaluate the similarity among features. Finally, particle filter algorithm is combined to realize target tracking. The result shows that the feature obtained by multi-stage convolution filtering can express target better, the proposed algorithm has a better inflexibility to illumination, covering, rotation, and camera shake, and it exhibits very good robustness in video sequence with such characteristics.
- Visual tracking,
- Deep learning,
- Principal Component Analysis (PCA),
- Convolutional neural network,
- Particle filter

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References(23)

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