Intelligent Transport Surveillance Memory Enhanced Method for Detection of Abnormal Behavior in Video

Abstract

The purpose is to build a better intelligent transport platform and improve the performance of surveillance video abnormal behavior detection systems under rapid progress of science and technology, to process large-scale traffic surveillance video data. Autoencoder (AE) can detect abnormal behavior by using reconstruction error information. However, it cannot decode some abnormal codes well, so an AE based on memory needs improvement. The objective of this research is to propose a model where abnormal surveillance video can be handled. Therefore, a self-coding method based on memory enhancement is proposed. The steps are as follows: different abnormal behavior detection system algorithms are analyzed at first. The characteristics of three different methods, namely, the original autoencoder (AE), recurrent neural network, and convolutional neural network, are compared. Then, a memory module is proposed to enhance the automatic encoder to reduce the reconstruction error of normal samples and increase the reconstruction error of abnormal samples. The effect image is obtained by Laplace transform and convolution for the image with low definition, and the image with noise is processed by guided filtering. Finally, different methods are used for experimental comparison. Experiments show that, on the dataset Avenue, the frame-level result of the method proposed is about 2% higher than that of the optimal ConvLSTM in the comparison method; on the Ped1 and Ped2 datasets, it is also about 3% higher than ConvLSTM. The comparison of different methods shows that the effect of the method proposed is the best. The self-coding traffic surveillance video abnormal behavior detection system based on memory enhancement is designed with a modular structure and it uses the self-coding method based on memory enhancement. The effectiveness of the proposed method in the real scene is verified by comparing the performance of different methods in the same data set (Xia and Li, 2021).