Anomaly detection and recognition of video surveillance images based on deep learning

Abstract

In order to solve the problems of pool generalization ability of traditional algorithms and high cost of manual inspection for abnormal image detection in remote video surveillance, this paper proposes an algorithm for abnormal image detection in video surveillance based on deep learning. First, the convolutional neural network based on VGG-16 uses the he_normal method to initialize the weights, and then the self-made datasets is preprocessed and input into the convolutional neural network for training, and finally an image for detecting video surveillance is obtained Model of abnormal interference. Experimental results show that this method can detect abnormal interference such as overexposure of brightness, color distortion, and video freezes in video surveillance, with an accuracy rate of 86%.