Deep Learning-based Feature Fusion for Action Recognition Using Skeleton Information

Abstract

Various action recognition systems have been proposed, but most of them are not feasible to be used in real-time applications. Skeleton-based action recognition has a low computational cost and is not affected by background changes. As the pose estimation models are becoming faster (almost real-time), a model was created with only 1.8M parameters named DD-net, which uses the skeleton information to predict the action. Recently an improved version of the model came out and was named TD-net. The model is very rich with geometric-based features but lacks motion-based features. To overcome this we added two motion features in the model named acceleration and velocity. These features were created using second order Taylor's approximation, in a window around the current frame. The model accuracy was compared with DD-net, TD-net, and state-of-the-art algorithms using three different datasets. An increase in accuracy is observed for all three datasets (i.e 1.1% for SHERC, 1.7% for FPHAB and 2% for JHMDB) when compared with TD-net.