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Research on Visual Tracking Algorithm Based on Deep Feature Expression and Learning |
Li Huan-yu①② Bi Du-yan① Yang Yuan② Zha Yu-fei① Qin Bing① Zhang Li-chao① |
①(College of Aerospace Engineering, Air Force Engineering University, Xi’an 710038, China)
②(College of ATC Navigation, Air Force Engineering University, Xi’an 710051, China) |
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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.
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Received: 06 January 2015
Published: 29 June 2015
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Corresponding Authors:
Yang Yuan
E-mail: kgd_bsh@163.com
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