Hand Segmentation

Hand segmentation, the task of isolating hand regions from images or video, is crucial for various applications, from human-robot collaboration to sign language recognition and egocentric activity understanding. Current research emphasizes developing robust methods that handle challenging conditions like occlusion, varying lighting, and diverse backgrounds, often employing deep learning models such as convolutional neural networks (CNNs) and Vision Transformers (ViTs), sometimes combined with techniques like domain randomization or multi-modal input (RGB-D). The availability of high-quality, publicly accessible datasets, particularly those capturing real-world industrial or surgical settings, is driving progress and enabling the development of more accurate and reliable hand segmentation algorithms with significant implications for human-computer interaction and other fields.