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Proceedings Paper

A method of adaptive learning rate tracking for embedded device based correlation surface evaluation
Author(s): Teng Zhang; Siyu Zhang; Zhimin Li; Sheng Zhong
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Paper Abstract

The Accuracy of correlation filtering trackers have got great improvement because of using high dimension features, but its real-time performance became worsen. And we often have the meet of running tracker on embedding device, in this case, we need less calculation. It is all known that the model updating strategy is also important for tracking performance. The fixed learning rate model updating strategy is difficult to deal with the situation that the object changes rapidly or slowly. For the problem, a new correlation surface quality evaluation metric is proposed in this paper. Meanwhile, we consider the occlusion of the object, and propose the occlusion judgment algorithm. Finally, the learning rate of model is updated adaptively according to the change speed of the object and whether the object is occluded. We further conduct experiment on the OTB50 dataset. Experimental results show that the correlation tracker with gray feature can improve the tracking accuracy by about 3% compared with MOSSE tracker, after adopting the learning rate adaptive strategy proposed in this paper and maintain high speed on embedding device.

Paper Details

Date Published: 14 February 2020
PDF: 9 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300I (14 February 2020);
Show Author Affiliations
Teng Zhang, Science and Technology on Mutlispectral Information Processing Lab. (China)
Siyu Zhang, Huazhong Univ. of Science and Technology (China)
Zhimin Li, Huazhong Univ. of Science and Technology (China)
Sheng Zhong, Science and Technology on Mutlispectral Information Processing Lab. (China)
Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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