3D Human Skeleton Data Compression for Action Recognition Skeletonbased action recognition continues to open up new application scenarios with the popularity of acquisition devices. This also leads to a rapid increase in the amount of human skeleton data. Currently, there is no skeleton data compression algorithm for the task of action recognition. In order to solve this problem, we propose the first skeleton data compression algorithm, which can compress the skeleton data stream to a small bandwidth while keeping the accuracy of action recognition as high as possible. The proposed compression algorithm is called Motionbased Joints Selection (MJS). It performs compression based on the amount of movement of different joints. In addition, we also explored the combination of MJS and existing lossless compression methods, and found the most suitable one. In the end, we verify that our compression method MJS can achieve promising results on the large dataset NTURGBD.

Action Recognition Based on 3D Skeleton and RGB Frame Fusion Action recognition has wide applications in assisted living, health monitoring, surveillance, and humancomputer interaction. In traditional action recognition methods, RGB videobased ones are effective but computationally inefficient, while skeletonbased ones are computationally efficient but do not make use of lowlevel detail information. This work considers action recognition based on a multimodal fusion between the 3D skeleton and the RGB image. We design a neural network that uses a 3D skeleton sequence and a single middle frame from an RGB video as input. Specifically, our method picks up one frame in a video and extracts spatial features from it using two attention modules, a selfattention module and a skeletonattention module. Further, temporal features are extracted from the skeleton sequence via a BILSTM subnetwork. Finally, the spatial features and the temporal features are combined via a feature fusion network for action classification. A distinct feature of our method is that it uses only a single RGB frame rather than an RGB video. Accordingly, it has a lightweighted architecture and is more efficient than RGB videobased methods. Comparative evaluation on two public datasets, NTURGBD and SYSU, demonstrates that, our method can achieve competitive performance compared with stateoftheart methods.