Generative Cooperative Learning for Unsupervised Video Anomaly Detection

Authors

M. Zaigham Zaheer, Electronics And Telecommunications Research Institute & Univ. Of Science And Technology & ETH Zurich, Switzerland & Mohamed Bin Zayed University Of Artificial Intelligence
Arif Mahmood, Information Technology University, Lahore, Pakistan
Muhammad Haris Khan, Mohamed bin Zayed University of Artificial IntelligenceFollow
Mattia Segù, ETH Zurich, Switzerland
Fisher Yu, ETH Zurich, Switzerland
Seung-Ik Lee, Electronics And Telecommunications Research Institute & Univ. Of Science And Technology

Document Type

Conference Proceeding

Publication Title

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Abstract

Video anomaly detection is well investigated in weakly-supervised and one-class classification (OCC) settings. However, unsupervised video anomaly detection methods are quite sparse, likely because anomalies are less frequent in occurrence and usually not well-defined, which when coupled with the absence of ground truth supervision, could adversely affect the performance of the learning algorithms. This problem is challenging yet rewarding as it can completely eradicate the costs of obtaining laborious annotations and enable such systems to be deployed without human intervention. To this end, we propose a novel unsupervised Generative Cooperative Learning (GCL) approach for video anomaly detection that exploits the low frequency of anomalies towards building a cross-supervision between a generator and a discriminator. In essence, both networks get trained in a cooperative fashion, thereby allowing unsupervised learning. We conduct extensive experiments on two large-scale video anomaly detection datasets, UCF crime and ShanghaiTech. Consistent improvement over the existing state-of-the-art unsupervised and OCC methods corroborate the effectiveness of our approach. © 2022 IEEE.

First Page

14724

Last Page

14734

DOI

10.1109/CVPR52688.2022.01433

Publication Date

9-27-2022

Keywords

Large dataset, Learning algorithms, Machine learning, Computer Vision and Pattern Recognition (cs.CV), Anomaly detection

Comments

Open access version, available at Computer Vision Foundation.

Recommended Citation

M. Z. Zaheer, A. Mahmood, M. H. Khan, M. Segu, F. Yu and S. -I. Lee, "Generative Cooperative Learning for Unsupervised Video Anomaly Detection," 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 2022, pp. 14724-14734, doi: 10.1109/CVPR52688.2022.01433.