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SiamOT: An Improved Siamese Network with Online Training for Visual Tracking.

Xiaomei Gong¹, Yuxin Zhou¹, Yi Zhang¹

¹Department of Computer Science, Sichuan University, Chengdu 610017, China.

Sensors (Basel, Switzerland)

|September 9, 2022

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This study introduces SiamOT, a novel twin-branch Siamese network for visual tracking. SiamOT enhances tracking accuracy by integrating online learning to capture both target and background information, improving performance on unknown targets.

Area of Science:

Computer Vision
Artificial Intelligence

Background:

Siamese networks are effective for visual tracking using convolutional neural networks and weight-sharing.
Offline training strategies in current Siamese networks struggle with unknown targets due to inability to learn background information, leading to performance degradation and tracking failures.

Purpose of the Study:

To address the limitations of existing Siamese networks in handling unknown targets and background interference.
To propose a novel twin-branch architecture, SiamOT, for robust visual tracking.

Main Methods:

Developed a twin-branch architecture (SiamOT) comprising a classical Siamese network and an online training branch.
The online branch incorporates feature fusion and an attention mechanism to capture and update both target and background information.

Keywords:

Siamese networks online training visual tracking

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Refined target descriptions by integrating online-learned background context.

Main Results:

SiamOT demonstrated superior performance on three mainstream benchmarks.
The proposed architecture showed stronger target discrimination abilities compared to existing methods.
Ablation studies validated the effectiveness of the individual components of SiamOT.

Conclusions:

SiamOT effectively mitigates performance degradation in visual tracking caused by unknown targets and background interference.
The integration of an online training branch with feature fusion and attention mechanisms enhances target description and tracking robustness.
SiamOT represents a significant advancement in Siamese network-based visual tracking, offering improved accuracy and reliability.