Dynamic Image Representations for Crowd Anomaly Detection using Generative Adversarial Networks.

Anomaly detection within crowded environments is a key challenge in the crowd behaviour understanding and computer vision fields. Application of crowd anomaly detection has improved recently, however, advancements of accuracy and computation (processing power and time) are still required. In this paper, we present an approach to crowd behaviour anomaly detection using dynamic image representations as an alternative to optical flow extractions for temporal development feature extraction. The features are used in conjunction with image-to-image translation using CGANs for anomaly detection within crowds, the proposed framework is evaluated on standard benchmark datasets. The experimental results obtained have demonstrated the efficacy of this approach in comparison to the state-of-the-art crowd anomaly detection methods.