Hand Detection

Hand detection in computer vision aims to accurately locate and identify hands in images and videos, supporting applications ranging from human-computer interaction to medical procedures and robotics. Current research emphasizes robust hand detection in challenging real-world scenarios (e.g., occlusion, varying lighting), often employing deep learning models like convolutional neural networks and transformers, along with multimodal approaches integrating RGB, depth, and even thermal data. These advancements are driving improvements in areas such as hand pose estimation, gesture recognition, and hand-object interaction analysis, with significant implications for fields like healthcare, virtual reality, and assistive technologies.