AED-Net: An Abnormal Event Detection Network
This addresses the problem of detecting anomalies in surveillance videos for security applications, but it is incremental as it builds on existing methods with a self-supervised approach.
The paper tackles abnormal event detection in crowded scenes by proposing a self-supervised framework called AED-Net, which combines PCAnet and kPCA, achieving competitive results with higher EER and AUC on UMN and UCSD datasets.
It is challenging to detect the anomaly in crowded scenes for quite a long time. In this paper, a self-supervised framework, abnormal event detection network (AED-Net), which is composed of PCAnet and kernel principal component analysis (kPCA), is proposed to address this problem. Using surveillance video sequences of different scenes as raw data, PCAnet is trained to extract high-level semantics of crowd's situation. Next, kPCA,a one-class classifier, is trained to determine anomaly of the scene. In contrast to some prevailing deep learning methods,the framework is completely self-supervised because it utilizes only video sequences in a normal situation. Experiments of global and local abnormal event detection are carried out on UMN and UCSD datasets, and competitive results with higher EER and AUC compared to other state-of-the-art methods are observed. Furthermore, by adding local response normalization (LRN) layer, we propose an improvement to original AED-Net. And it is proved to perform better by promoting the framework's generalization capacity according to the experiments.