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SiamFDA: feature dynamic activation siamese network for visual tracking
Jialiang Gu1, Ying She2, Yi Yang2
1Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510000, China. gujliang@mail2.sysu.edu.cn.
Scientific Reports
|February 27, 2024
Summary
This study introduces the Feature Dynamic Activation Siamese Network (SiamFDA), a novel visual tracking framework. SiamFDA enhances tracking by capturing global spatial information and improving robustness in challenging scenarios.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Current Siamese network-based visual tracking algorithms often overlook global spatial information.
- This limitation leads to reduced robustness, particularly in scenarios with unreliable object regions.
Purpose of the Study:
- To introduce a novel anchor-free visual tracking framework, the Feature Dynamic Activation Siamese Network (SiamFDA).
- To address the limitations of existing methods by incorporating global spatial information and enhancing robustness.
Main Methods:
- Developed SiamFDA, a framework that captures long-range dependencies between distant pixels.
- Implemented a hierarchical feature selector for adaptive feature activation across different layers.
- Introduced an adaptive sample label assignment method to optimize training.
Main Results:
- SiamFDA demonstrated superior performance compared to state-of-the-art trackers across six benchmark datasets (VOT-2018, VOT-2019, GOT10k, LaSOT, OTB-2015, OTB-2013).
- The framework achieves a real-time processing speed of 40 frames per second.
- Achieved improved robustness in challenging tracking scenarios.
Conclusions:
- SiamFDA offers a significant advancement in anchor-free visual tracking.
- The proposed methods effectively leverage global spatial information and adaptive feature selection for enhanced performance.
- SiamFDA provides a robust and efficient solution for real-time visual object tracking.
