Human Behavior Recognition from Multiview Videos Abstract   With the proliferation of deep learning techniques, a significant number of applications related to home care systems have emerged recently. In particular, detecting abnormal events in a smart home environment has been extensively studied. In this paper, we adopt deep learning techniques, including convolutional neural networks (CNNs) and long shortterm memory (LSTM) networks, to construct deep networks to learn the longterm dependencies from videos for human behavior recognition in a multiview framework. We adopt two cameras as our sensors to efficiently overcome the problem of occlusions and contour ambiguity for improving the accuracy performance of the multiview framework. After performing a series of image preprocessing on the raw data, we obtain human silhouette images as the input to our training model. In addition, because realworld datasets are complicated for analysis, labeling data is time consuming. Therefore, we present an image clustering method based on a stacked convolutional autoencoder (SCAE), which generates clustering labels for autolabeling. Finally, we set up our experimental environment as a normal residence to collect a large dataset, and the experimental results demonstrate the novelty of our proposed models.

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.