[1]WU Guishan,LIN Shubin,ZHONG Jianghua,et al.Regional loss function based siamese network for object tracking[J].CAAI Transactions on Intelligent Systems,2020,15(4):722-731.[doi:10.11992/tis.201910005]
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Regional loss function based siamese network for object tracking

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 4 Page number: 722-731 Column: 学术论文—机器学习 Public date: 2020-07-05
Title:
Regional loss function based siamese network for object tracking
Author(s):
WU Guishan1; 2;  LIN Shubin1; 2;  ZHONG Jianghua3;  YANG Wenyuan1; 2
1. School of Computer Science, Minnan Normal University, Zhangzhou 363000, China;
2. Fujian Key Laboratory of Granular Computing and Application, Minnan Normal University, Zhangzhou 363000, China;
3. Information and Network Center, Minnan Normal University, Zhangzhou 363000, China
Keywords:
computer vision; object tracking; regional loss; depth features; siamese network; convolutional neural network; back propagation; VGG network
CLC:
TP391.4
DOI:
10.11992/tis.201910005
Abstract:
Due to the low spatial resolution of deep features extracted by pre-trained convolutional neural network, fast motion causes loss of spatial details of a moving object. This paper proposes a method to construct a siamese network for object tracking, so as to reduce the redundancy between the deep feature channels and the loss of high-level information. First, the VGG-16 convolutional neural network is trained offline to extract deep features and form the initial deep feature space. And then, the regional loss function is used to construct the feature and scale selection network. The feature is selected according to the gradient size of back propagation. Further, the selected features are spliced and integrated into the siamese network for matching tracking. By comparing OTB-2013, OTB-2015, VOT2016 and TempleColor benchmark datasets with other algorithms, it shows that the algorithm has preferable precision and robustness in the challenging scenarios such as fast motion and low resolution.
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Last Update: 2020-07-25

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