Local Patch Tracking Algorithm Based on Fast Fourier Transform

Hou Zhi-qiang; Zhang Lang; Yu Wang-sheng; Xu Wan-jun

doi:10.11999/JEIT150183

Volume 37 Issue 10

Sep. 2015

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Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(10): 2397-2404

Hou Zhi-qiang, Zhang Lang, Yu Wang-sheng, Xu Wan-jun. Local Patch Tracking Algorithm Based on Fast Fourier Transform[J]. Journal of Electronics & Information Technology, 2015, 37(10): 2397-2404. doi: 10.11999/JEIT150183

Citation:

Hou Zhi-qiang, Zhang Lang, Yu Wang-sheng, Xu Wan-jun. Local Patch Tracking Algorithm Based on Fast Fourier Transform[J]. Journal of Electronics & Information Technology, 2015, 37(10): 2397-2404. doi: 10.11999/JEIT150183

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Local Patch Tracking Algorithm Based on Fast Fourier Transform

doi: 10.11999/JEIT150183

Funds:

The National Natural Science Foundation of China (61175029, 61473309)

Received Date: 2015-02-02
Rev Recd Date: 2015-06-03
Publish Date: 2015-10-19

Abstract

Abstract

In order to solve the problems of appearance change, local occlusion and background distraction in the visual tracking, a local patch tracking algorithm based on Fast Fourier Transform(FFT)is proposed. The tracking precision can be improved by establishing objects patch kernel ridge regression model and using patch exhaustive search based on circular structure matrix, and the efficiency can be improved by transforming time domains operation into frequency domains based on FFT. Firstly, patch kernel ridge regression model is constructed according to the initialized tracking area. Secondly, a patch exhaustive search method based on circular structure matrix is proposed, then the position model is constructed in adjoining frame. Finally, the position of the object is estimated accurately using the position model and the local patch model is updated. Experimental results indicate that the proposed algorithm not only can obtain a distinct improvement in coping with appearance change, local occlusion and background distraction, but also have high tracking efficiency.
- Visual tracking,
- Kernel ridge regression model,
- Fast Fourier Transform (FFT),
- Patch exhaustive search,
- Position model

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References(16)

References

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