Skeleton Dataset

Skeleton datasets are collections of 3D human pose data, primarily used for training and evaluating algorithms in action recognition, gait analysis, and person re-identification. Current research focuses on improving model robustness in challenging scenarios, such as handling imbalanced datasets (long-tailed distributions) and open-set recognition (identifying unknown actions). This involves developing sophisticated architectures like graph convolutional networks (GCNs), transformers, and recurrent neural networks (RNNs), often incorporating techniques such as contrastive learning and multi-modal fusion to enhance performance. The advancements in this field have significant implications for applications ranging from healthcare (rehabilitation assessment) to security (gait recognition) and human-computer interaction.