Smoking Behavior Detection Based on Hand Trajectory Tracking and Mouth Saturation Changes Smoking seriously endanger peopleu0027s physical and mental health. In some special places, e.g., gas stations and forest areas, smoking may cause serious accidents. In order to detect smoking behavior timely and accurately, we propose an effective and practical detection method by means of video analysis. Two basic approaches are involved in the proposed method: one is smoke detection; the other is smoking action detection. In the first approach, we detect human face by using a special open source of face detection system called SeetaFace, then segment the mouth area and calculate the corresponding grayscale and saturation, finally we can determine if sudden a change occurs around the mouth. In the second approach, there are two basic steps: firstly, we detect the skin color area based on the skin color ellipse model in YCrCb color space, then determine the initial position of the hand by using the location of the skin color area that relative to the face; secondly, track the trajectory of hand movement by using optical flow and then detect whether the hand overlap the mouth in real time. Finally, we combine the results of the preceding two steps in the second approach with the result in the first approach together and then we can determine smoking or nonsmoking person. The experimental results show that the proposed method can effectively detect smoking behavior with a small training sample in real time and achieve the detection rate of 95%.

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.