CCTV Image Sequence Generation and Modeling Method for Video Anomaly Detection Using Generative Adversarial Network

Abstract

Video anomaly detection is one of the most attractive problem in various fields likes computer vision. In this paper, we propose a VAD classifier modeling method that learns in a supervised learning manner. The basic idea is to solve the problem of labeled data shortage through transfer learning. The key idea is to create an underlying model of transfer learning through the GAN of discriminator. We solved this problem by proposing a GAN model consisting of a generator that generates video sequences and a discriminator that follows LRCN structure. As a result of the experiment, The VAD classifier learned through GAN-based transfer learning obtained higher accuracy and recall than the pure LRCN classifier and other machine learning methods. Additionally, we demonstrated that the generator be able to stably generate the image similar to the actual data as the learning progressed. To the best of our knowledge, this paper is the first case to solve the VAD problem using the GAN model and the supervised learning manner.