(SSTA-NET)Speed and Short-Term Action Learning Networks for Indoor Wind Tunnel Skydiving Action Recognitions

Action recognition is widely used in video classification and motor behavior analysis. However, existing action recognition methods often overlook the importance of speed and distinct short-term actions. In particular, the similarity between various short-term actions during skydiving training poses a significant challenge for action recognition in indoor wind tunnel skydiving. We tackle these challenges by proposing a Speed and Short-Term Action Learning Network (SSTA-NET) for indoor wind tunnel skydiving action recognition. The network integrates a short-term action feature attention module that processes short-term action information within a single framework. To acquire dynamic motion data across varying movement types, SSTA-NET introduces a displacement map speed learning module that captures motion speed details in action videos. Our network also utilizes a video-efficient multi-scale attention module, designed to prioritize salient visual areas while reducing redundant spatio-temporal redundancy in the videos. SSTA-NET achieves an average improvement of 8.56% on the indoor wind tunnel skydiving dataset and 6.06% on the HMDB51 public dataset compared to other methods.