Gait Dataset

Gait datasets are collections of data representing human walking patterns, used primarily for research in gait recognition and analysis. Current research focuses on improving the accuracy and robustness of gait recognition systems, exploring deep learning architectures like convolutional neural networks (CNNs) and transformers, and developing self-supervised learning techniques to leverage large unlabeled datasets. This work has implications for various applications, including security (authentication), healthcare (abnormal activity detection and clinical gait analysis), and even virtual environments (avatar creation and animation), highlighting the importance of developing accurate, efficient, and privacy-preserving gait analysis methods.