AVID: Adversarial Visual Irregularity Detection

TL;DR

AVID introduces an adversarial deep learning framework that effectively detects and localizes irregularities in videos and images without requiring extensive irregular training samples, outperforming existing methods.

Contribution

The paper presents a novel end-to-end GAN-based architecture for unsupervised irregularity detection and localization in videos, combining pixel-level inpainting and patch-level detection.

Findings

Outperforms state-of-the-art on three datasets

Effective in fine segmentation of irregularities

Works without extensive irregular training data

Abstract

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 Inpainter, and the other works as a patch-level Detector. 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 show that our method can outperform the state-of-the-art and fine-segment the irregularity.