Occlusion Avoidance

Occlusion avoidance focuses on developing methods to reliably detect and handle situations where objects are partially or fully hidden from view, a critical challenge across various fields like computer vision and robotics. Current research emphasizes using depth information, generative adversarial networks (GANs), and other deep learning architectures to improve object detection and tracking in occluded scenes, often incorporating techniques like feature completion and probabilistic modeling to address uncertainty. These advancements are crucial for improving the robustness and safety of autonomous systems, enhancing the accuracy of image-based tasks, and advancing explainable AI (XAI) by addressing limitations in occlusion-based explanation methods.