Abstract

Video anomaly detection is well investigated in weakly supervised and one-class classification (OCC) settings. However, unsupervised video anomaly detection is 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 convergence of 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 facilitating the overall convergence. 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.

Visualization of Learning Procedures

Paper

M. Zaigham Zaheer, Arif Mahmood, M. Haris Khan, Mattia Segu, Fisher Yu, Seung-Ik Lee Generative Cooperative Learning for Unsupervised Video Anomaly Detection CVPR 2022

Citation

@inproceedings{zaheer2022gcl,
    author    = {Zaheer, Muhammad Zaigham and Mahmood, Arif and Khan, Muhammad Haris and Segù, Mattia and Yu, Fisher and Lee, Seung-Ik},
    title     = {Generative Cooperative Learning for Unsupervised Video Anomaly Detection},
    booktitle = {Computer Vision and Pattern Recognition},
    year      = {2022}
}