Learning with few labels for skeleton-based action recognition - Yang, Siyuan (Nanyang Technological University, 2024)
Human Action Recognition, which involves discerning human actions, is vital for many real-world applications. Skeleton sequences, tracing human body joint trajectories, capture essential human motions, making them appropriate for action recognition. Compared to RGB videos or depth data, 3D skeleton data offers concise representations of human behaviors, proving robust against appearance variations, distractions, and viewpoint changes. This has led to increased interest in skeleton-based action recognition research.