Methodology for Object Tracking
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 372
Special Issue Editor
Interests: AI safety; computer vision; adversarial attack; adversarial defense
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Visual object tracking is a crucial technique in the field of vision intelligence, allowing the position and size of targeted objects to be accurately predicted in real-time video systems. It has been applied in various autonomous systems, including self-driving cars, unmanned aircraft, and robotics. State-of-the-art tracking methods now achieve high accuracy on challenging datasets by utilizing fully trained deep neural networks (DNNs). However, existing tracking methods often overlook the reliability of deep trackers, leaving them vulnerable to common corruptions and adversarial attacks. In recent years, a series of studies have emerged with the aim of attacking visual object trackers, while others focus on developing defense mechanisms to enhance robustness against such attacks.
This Special Issue aims to collate recent developments of trustworthy AI techniques to address the reliability issues in the real world and to provide researchers around the world with an opportunity to present state-of-the-art results and literature reviews. Topics of interest to this Special Issue include (but are not limited to):
- Image/video object detection and tracking;
- Image/video saliency detection and tracking;
- Robust visual object tracking;
- Robust benchmark construction;
- Adversarial attack techniques against detection and tracking;
- Robust learning or optimization algorithms;
- Data augmentation techniques for robustness enhancement;
- Data restoration against attacks and common corruptions;
- Explainable methods for trustworthy AI;
- Uncertainty machine learning.
Dr. Qing Guo
Guest Editor
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