Self-supervised discriminative model prediction for visual tracking

The discriminative model prediction (DiMP) object tracking model is an excellent end-to-end tracking framework and have achieved the best results of its time. However, there are two problems with DiMP in the process of actual use: (1) DiMP is prone to interference from similar objects during the tra...

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Bibliographic Details
Published in:Neural computing & applications 2024-04, Vol.36 (10), p.5153-5164
Main Authors: Yuan, Di, Geng, Gu, Shu, Xiu, Liu, Qiao, Chang, Xiaojun, He, Zhenyu, Shi, Guangming
Format: Article
Language:English
Subjects:
Artificial Intelligence
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Datasets
Image Processing and Computer Vision
Interference
Localization
Methods
Optical tracking
Original Article
Performance evaluation
Probability and Statistics in Computer Science
Robustness
Tracking
Training
Visual discrimination
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Summary:The discriminative model prediction (DiMP) object tracking model is an excellent end-to-end tracking framework and have achieved the best results of its time. However, there are two problems with DiMP in the process of actual use: (1) DiMP is prone to interference from similar objects during the tracking process, and (2) DiMP requires a large amount of labeled data for training. In this paper, we propose two methods to enhance the robustness of interference to similar objects in target tracking: multi-scale region search and Gaussian convolution-based response map processing. Simultaneously, aiming at tackling the issue of requiring a large amount of labeled data for training, we implement self-supervised training based on forward-backward tracking for the DiMP tracking method. Furthermore, a new consistency loss function is designed to better self-supervised training. Extensive experiments show that the enhancements implemented in the DiMP tracking framework can bolster its robustness, and the tracker based on self-supervised training has outstanding tracking performance.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-09348-5