A deep siamese network algorithm with template updating for object tracking

doi:10.3969/j.issn.1000-1565.2022.02.016

Abstract

Abstract: To solve the problem of inaccurate target location caused by scale variation, occlusion and motion blur during the tracking process, a deep Siamese network target tracking algorithm with high confidence template updating mechanism is proposed based on the SiamFC(fully convolutional siamese network). First, the main network uses ResNet-50 residual network for feature extraction and multi-layer feature maps for target prediction; second, a high confidence template updateing module is constructed to avoid the template drift caused by frequent updating. Experimental results indicate that the success rate and tracking accuracy of the proposed algorithm are increased by about 3.4% and 2.6% respectively compared to the benchmark algorithm when running on the dataset of OTB100. The experiments under various challenging factors show that the proposed algorithm has good robustness, which can resist the effects of various complex factors effectively such as target occlusion, scale variation and motion blur.

Key words: object tracking, feature extraction, feature fusion, siamese network, SiamFC

CLC Number:

TP391

CHEN Liping, YUAN Tongtong, YANG Wenzhu, CHEN Xiangyang, WANG Sile. A deep siamese network algorithm with template updating for object tracking[J]. Journal of Hebei University(Natural Science Edition), 2022, 42(2): 217-224.

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