Compact Video Analysis Human Action Recognition Approach Human action recognition is one of the most challenging computer vision topics and an important realistic yet open research task in many relevant applications such as security, content retrieval and video surveillance. In this paper, we proposed a robust approach for human action recognition in different video scenes. The architecture of the approach consists of shot boundary detection, shot frame rate resampling, extracting compact feature vectors by emphasizing variation and classification. Extracting strong patterns in feature vectors before classification affect the chosen approach high performance compared with latest stateofart work using four widely used realistic data sets: Hollywood2, UCF11(YouTube actions), UCF Sports and KTH.

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.