An anomaly detection method using deep convolution neural network for vision image of robot

With the acceleration of urbanization, growing number of places are crowded with people, such as banks, shopping malls, schools and hospitals, and the incidence of abnormal events such as assault, fighting, trampling and evacuation is also increasing. Therefore, the need for intelligent detection and identification of abnormal events by security early warning robots is attracting much more attention. Aiming at the problem of anomaly detection for security early warning robots, an anomaly detection method using wireless vision sensor network (WVSN) and deep learning is proposed. Firstly, image collection is carried out by WVSN, and video image information in the monitoring range is transmitted and stored by WVSN. Then, the collected image is preprocessed, and the possible abnormal areas are effectively extracted by region of interest (ROI), image filtering and region segmentation. Finally, the abnormal areas are extracted by WVSN. The slow feature analysis (SFA) is used to solve the problem of insufficient training samples in the deep neural network. Furthermore, the deep convolution neural network (CNN) and the support vector machine (SVM) are used to train and complete the classification respectively. The experimental results on UMN and PETS 2009 database show that the abnormal events can be effectively detected by the proposed method. Compared with several other advanced methods, the proposed method has higher detection accuracy and area under the curve (AUC). Among them, AUC on the experimental data set can reach up to 0.998. Therefore, the proposed method has a good reference value for the application of security early warning robots in densely populated places.