Skip to Main Content
A community intrusion detection system based on wavelet neural network (WNN) is presented in this paper. This system is composed of ARM (Advanced RISC Machines) data acquisition nodes, wireless mesh network and control centre. The data acquisition node uses sensors to collect information and processes them by image detection algorithm, and then transmits information to control centre with wireless mesh network. When there is abnormal phenomenon, the system starts the camera and the WNN is used to recognize the face image. We adopt a algorithm of reduce the number of the wavelet basic function by analysis the sparseness property of sample data which can optimize the wavelet network, and give the network learning algorithm. With the ability of strong pattern classification and function approach and fast convergence of WNN, the recognition method can truly classify the face. This system resolves the defect and improves the intelligence and alleviates worker's working stress.