View Action Recognition

View action recognition focuses on accurately identifying human actions from multiple camera viewpoints, overcoming challenges like viewpoint variations, occlusions, and background clutter. Current research emphasizes robust feature extraction using architectures like transformers and convolutional neural networks, often incorporating techniques such as attention mechanisms and contrastive learning to improve view invariance and disentangle action from view-specific information. This field is significant for advancing computer vision capabilities in applications such as video surveillance, human-computer interaction, and assisted living, where multi-view data is readily available but presents unique analytical challenges. The development of large-scale multi-view datasets and novel algorithms to handle weak labels are also key areas of ongoing investigation.