## 1 Introduction
The CNN-based HAR models that were mentioned can be used in various fields that require actual HAR through the action recognition performance that improved compared with existing techniques, but owing to the complexity of the model and long data processing time, there are still limitations to be operated in real-time in low-cost embedded systems [20]. Recently, HAR has been applied to integrated monitoring systems, which detect emergency situations with CCTVs. Most of integrated monitoring systems receive multiple CCTV images through network-based digital video communication and extract monitoring information [21] as shown in Figure 1. The video data transmission process is used to recognize behavior in a server PC equipped with a GPU that has strong computational efficiency owing to the complexity of the HAR model. Therefore, HAR models have a limitation in recognizing behavior in real-time, being installed in a single embedded system directly connected to the CCTV. Figure 1: An example of a wireless communication network for recognizing behavior through CCTV using a complex HAR model. In particular, it is essential for the system to recognize human action quickly in urgent situations related to the safety, such as violence and theft on the streets, and in emergencies, such as detecting urgent situations or abnormal behaviors of the elderly and single-person households [22]. When the action is recognized through the HAR model in the embedded system where the camera is installed, time and cost in the process of transmitting high-capacity video data can be reduced [23]. Therefore, in this paper, we propose a weighted mean-based single-stream CNN model that recognizes action faster than conventional models. As the proposed method has a simpler structure than the existing CNN-based models with high complexity, recognizing behavior in a single embedded system connected to CCTV without transmitting image data through network communication is possible. The main idea is to build a lightweight CNN-based HAR model that can be applied to low-cost embedded systems with low-end GPUs to apply HAR to CCTV-based surveillance fields and to recognize instantaneous motions in emergency situations at high speed. The proposed system extracts spatial and temporal features by weight and averaged the change rate of frames according to time on a spatial feature map extracted by CNN. The weighted mean is calculated sequentially by the change rate of each frame extracted according to any time interval from the video and the change rate of frame at the corresponding time is weighted in the process of averaging feature maps, so the spatial and temporal features are created in the contexts of the integrated time-varying motion representation. Spatial feature maps at a specific time where the amount of motion change is high are weighted more than the point of time when the amount of change is low and the context of the motion representation is created; spatial representations are extracted efficiently at the point of time affecting the recognition performance of systems. The weighted mean also recognizes action by processing data at a rapid rate through a lighter structure than conventional CNN-based HAR models. 3D CNNs extract temporal features using spatiotemporal filters, two-stream CNNs utilize optical flows, and ConvLSTMs utilizes LSTMs to extract temporal features. Such models take a long time in the calculation process with an extended CNN structure combining networks that extracts temporal features in the existing two-dimensional CNN structure. However, the proposed model recognizes action by inputting a one-dimensional spatiotemporal feature vector into FC layers, where the vector is generated by spatial feature maps and frame change rate using weighted mean. Therefore, video data are processed at high speed while maintaining the existing two-dimensional CNN structure.