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Robust Template Adjustment Siamese Network for Object Visual Tracking
Chuanming Tang1,2,3, Peng Qin2,3, Jianlin Zhang2
1Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610200, China.
Sensors (Basel, Switzerland)
|March 6, 2021
Summary
This study introduces the Template Adjustment Siamese Network (TA-Siam) to improve visual tracking by adapting templates to target appearance changes, effectively preventing model drift and target loss in long-term sequences.
Area of Science:
- Computer Vision
- Machine Learning
- Artificial Intelligence
Background:
- Existing visual trackers often fail due to model degeneration, leading to drift and target loss.
- Appearance templates extracted from initial frames struggle with long-term target variations.
Purpose of the Study:
- To propose a novel visual tracking framework, the Template Adjustment Siamese Network (TA-Siam).
- To address model drift and target loss challenges in Siamese networks.
- To enhance tracking accuracy and robustness for long-term sequences.
Main Methods:
- Introduced TA-Siam, a framework with template adjustment and classification-regression subnetworks.
- The template adjustment module adaptively updates templates using subsequent frame features.
- Utilized rhombus labels to reduce classification errors and an effective regression loss for training.
Main Results:
- TA-Siam demonstrated state-of-the-art performance on challenging benchmarks (VOT2016, VOT2018, OTB50, OTB100, GOT-10K, LaSOT).
- Achieved a high tracking speed of 45 FPS.
- Effectively mitigated model drift and improved target localization accuracy.
Conclusions:
- The proposed TA-Siam framework significantly enhances visual tracking robustness and accuracy.
- Adaptive template adjustment is crucial for handling appearance variations in long-term tracking.
- TA-Siam offers a promising solution for real-time, high-performance visual object tracking.
