Pedestrian Spatio-Temporal Information Fusion For Video Anomaly Detection

TL;DR

This paper presents a novel pedestrian spatiotemporal information fusion method for video anomaly detection, enhancing temporal modeling and normal behavior differentiation using advanced neural network modules and loss functions.

Contribution

It introduces residual modules and a feature discretization loss to improve temporal modeling and normal behavior distinction in video anomaly detection.

Findings

Outperforms current mainstream methods on CUHK Avenue and ShanghaiTech datasets.

Effectively distinguishes different normal behavior patterns.

Achieves real-time detection performance.

Abstract

Aiming at the problem that the current video anomaly detection cannot fully use the temporal information and ignore the diversity of normal behavior, an anomaly detection method is proposed to integrate the spatiotemporal information of pedestrians. Based on the convolutional autoencoder, the input frame is compressed and restored through the encoder and decoder. Anomaly detection is realized according to the difference between the output frame and the true value. In order to strengthen the characteristic information connection between continuous video frames, the residual temporal shift module and the residual channel attention module are introduced to improve the modeling ability of the network on temporal information and channel information, respectively. Due to the excessive generalization of convolutional neural networks, in the memory enhancement modules, the hopping connections of each codec layer are added to limit autoencoders' ability to represent abnormal frames too vigorously and improve the anomaly detection accuracy of the network. In addition, the objective function is modified by a feature discretization loss, which effectively distinguishes different normal behavior patterns. The experimental results on the CUHK Avenue and ShanghaiTech datasets show that the proposed method is superior to the current mainstream video anomaly detection methods while meeting the real-time requirements.