Real-time detection of irregularities in visual data is very invaluable and useful in many prospective applications including surveillance, patient monitoring systems, etc. With the surge of deep learning methods in the recent years, researchers have tried a wide spectrum of methods for different applications. However, for the case of irregularity or anomaly detection in videos, training an end-to-end model is still an open challenge, since often irregularity is not well-defined and there are not enough irregular samples to use during training. In this paper, inspired by the success of generative adversarial networks (GANs) for training deep models in unsupervised or self-supervised settings, we propose an end-to-end deep network for detection and fine localization of irregularities in videos (and images). Our proposed architecture is composed of two networks, which are trained in competing with each other while collaborating to find the irregularity. One network works as a pixel-level irregularity $I$npainter, and the other works as a patch-level $D$etector. After an adversarial self-supervised training, in which $I$ tries to fool $D$ into accepting its inpainted output as regular (normal), the two networks collaborate to detect and fine-segment the irregularity in any given testing video. Our results on three different datasets (one synthetic and two video datasets) show that our proposed method can not only outperform the state-of-the-art but also to fine-segment the irregularity.

此篇研究提出了一种基于生成对抗网络和自监督学习的深度网络架构, 可实现视频中不规则区域的检测和定位, 并在三个不同数据集上表现出色。

对抗性视觉异常检测(AVID)