[1]JIANG Wentao,ZHANG Dapeng.Research on weak object tracking based on Siamese network with optimized classification[J].CAAI Transactions on Intelligent Systems,2023,18(5):984-993.[doi:10.11992/tis.202211043]
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Research on weak object tracking based on Siamese network with optimized classification

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 5 Page number: 984-993 Column: 学术论文—机器学习 Public date: 2023-09-05
Title:
Research on weak object tracking based on Siamese network with optimized classification
Author(s):
JIANG Wentao1;  ZHANG Dapeng2
1. College of Graduate School, Liaoning Technical University, Huludao 125105, China;
2. Graduate School, Liaoning Technical University, Huludao 125105, China
Keywords:
computer vision; object tracking; weak object; deformable convolution; prior spatial score; localization quality score; feature extraction; convolutional neural network; siamese network
CLC:
TP391.4
DOI:
10.11992/tis.202211043
Abstract:
To address the problem that traditional Siamese networks are poor in tracking weak objects in blurry and low resolution, this study proposed a Siamese network with optimized classification prediction. First, the feature extraction ability of the backbone network was improved by introducing a deformable convolution module. Second, this algorithm enhances the ability of the backbone network to extract features by introducing the location information in the classification branch. Finally, a lightweight convolutional neural network was used for the prediction of classification and boundary to further utilize the semantic information of the images while avoiding multiscale testing, making the tracking results more reliable. Many experiments have analyzed OTB2015 and VOT2018 datasets, and the results show that the comprehensive performance of this algorithm is better than those of the mainstream similar algorithms, demonstrating excellent adaptability to complex scenes such as motion blur, deformation, and fast motion.
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