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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Object tracking algorithm based on deformable attention mechanism.

Qiaoling Liu^1,2, Na Yu³, Jinfu Cheng⁴

¹School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu, 610106, China. liuqiaolingg2023@126.com.

Scientific Reports

|March 6, 2026

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This study introduces a new object tracking algorithm using a deformable attention mechanism to improve performance in challenging conditions like occlusion and motion. The method enhances feature extraction and fusion for more robust and accurate real-time tracking.

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Object tracking methods struggle with occlusion, illumination changes, and rapid motion.
Existing algorithms lack robustness in complex dynamic scenes.

Purpose of the Study:

To propose a novel object tracking algorithm robust to challenging environmental conditions.
To enhance the accuracy and adaptability of object tracking systems.

Main Methods:

Integrating a deformable attention module into the ResNet-18 feature extraction network.
Utilizing an improved Bidirectional Feature Pyramid Network for multi-scale feature fusion.
Incorporating dynamic Kalman filtering for motion state adaptability.

Main Results:

Keywords:

Bidirectional feature pyramid network Deformable attention mechanism Kalman filter Object tracking algorithm ResNet-18

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Achieved 61.5% overlap and 68.4% success rates on the GOT-10k dataset with low computational load (1.96 GFLOPs).
Attained 77.5% MOTA and 77.0% IDF1 on the MOT20 dataset.
Demonstrated superior tracking performance compared to existing algorithms in complex scenarios.

Conclusions:

The proposed deformable attention mechanism significantly improves object tracking robustness.
The algorithm offers an effective solution for real-time tracking in dynamic and complex environments.