Robust and efficient deep learning methods for vision-based action recognition - Xu, Yuecong (Nanyang Technological University, 2021)
Vision-based action recognition, which performs action recognition based solely on RGB frames, has received strong research interest thanks to its wide applications in various fields, e.g. surveillance, smart homes, and autonomous driving. Significant progress has been made in vision-based action recognition thanks to the development of recognition technologies, particularly deep learning methods which have proven their effectiveness in visual recognition tasks, such as image classification. Compared to static images, videos contain additional information due to the additional temporal dimension, which includes both temporal and spatiotemporal correlation features. Therefore, the key to robust and efficient action recognition lies in the effective and efficient utilization of the temporal and spatiotemporal correlation features embedded within videos.