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Scale adaptive correlation filter tracking based on the autocorrelation matrix
Author(s): Jiang Zhu; Yunyi Yan
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Paper Abstract

Target tracking is an important research in computer vision. It has wide applications in human-computer interaction, machine recognition and artificial intelligence. But most existing tracking methods can not calculate the target scale well, resulting in low tracking accuracy. Some scale adaptive algorithms calculate scale by multiple attempts, which greatly improves the computational complexity. For this problem, this paper proposed a new scale adaptive correlation filter tracking algorithm based on the autocorrelation matrix. The method is based on the circulant structure of tracking-bydetection with kernels(CSK). Firstly, the sample of each frame is constructed as a cyclic matrix, and the kernel recursive least square (KRLS) method is used to learn the classifier. FFT accelerates the convolution process and makes the tracking speed faster. Finally, calculate the autocorrelation matrix using the standard image of each frame during correlation filtering. And get the target scale through the mapping of features between autocorrelation matrix. The experimental results showed that our method can update target scale during real-time tracking and improve the tracking accuracy effectively. Comparing to other algorithms, our algorithm can quickly adapt target scale during tracking and perform better in accuracy and speed.

Paper Details

Date Published: 15 November 2018
PDF: 8 pages
Proc. SPIE 10964, Tenth International Conference on Information Optics and Photonics, 109640Q (15 November 2018); doi: 10.1117/12.2504572
Show Author Affiliations
Jiang Zhu, Xidian Univ. (China)
Yunyi Yan, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 10964:
Tenth International Conference on Information Optics and Photonics
Yue Yang, Editor(s)

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