Year: 2025
Type: article
Abstract: Visual object tracking is crucial for numerous applications ranging from smartphones to autonomous vehicles. However, the impact of input noise on tracking performance remains underexplored. This paper presents a lightweight neural network module designed to enhance the robustness of 2D tracking methods against various types of noise. By performing image-to-image translation, the proposed robust tracking module (RTM) standardizes the operational space of tracking algorithms, thereby improving their resilience. Experimental results on benchmark datasets demonstrate the effectiveness of RTM in mitigating performance degradation caused by noise. Additionally, we introduce an evaluation toolkit that facilitates the assessment of tracking robustness against common noise types. The source code of the proposed method is available at https://github.com/iason1907/RTM .
Source: The Visual Computer
Institution: Aristotle University of Thessaloniki
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,190
OpenAlex ID: W4409584239