Special Issue "Machine Learning in Robust Object Detection and Tracking"
Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 19172
The rapid development of deep learning techniques, visual detection and tracking has led to significant progress being made in the accuracy on diverse benchmarks. However, due to the complex situations in the real world (e.g., degradations caused by scene variations and sensor noises), existing detection and tracking methods usually run into problems and cannot achieve similar accuracies on the benchmarks. As a result, a series of works are developed to alleviate the robustness issues of the state-of-the-art detection and tracking methods.
This Special Issue aims to gather the recent developments of machine learning techniques to address the robustness issues in the real world and to provide researchers around the world with an opportunity to present state-of-the-art results as well as literature reviews.
Dr. Qing Guo
Manuscript Submission Information
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- image/video object detection
- image/video saliency detection
- image/video semantic segmentation
- robust visual object tracking
- robust benchmark construction
- adversarial attack techniques against detection, tracking, and segmentation
- robust learning or optimization algorithms
- data augmentation techniques for robustness enhancement
- data restoration against degradations
- explainable methods for deep learning