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

Wonsup Shin, Sung-Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings
EditorsHujun Yin, Paulo Novais, David Camacho, Antonio J. Tallón-Ballesteros
PublisherSpringer Verlag
Pages457-467
Number of pages11
ISBN (Print)9783030034924
DOIs
Publication statusPublished - 2018 Jan 1
Event19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 - Madrid, Spain
Duration: 2018 Nov 212018 Nov 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11314 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
CountrySpain
CityMadrid
Period18/11/2118/11/23

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Shin, W., & Cho, S-B. (2018). CCTV Image Sequence Generation and Modeling Method for Video Anomaly Detection Using Generative Adversarial Network. In H. Yin, P. Novais, D. Camacho, & A. J. Tallón-Ballesteros (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings (pp. 457-467). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11314 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-03493-1_48