Dynamic Image Representations for Crowd Anomaly Detection using Generative Adversarial Networks
Presented by Samar Mahmoud
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. The proposed framework presents 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 conditional generative adversarial networks (CGANs) for anomaly detection within crowds. The proposed framework is evaluated on standard benchmark datasets as well as the high-density dataset (AHDCrowd). The experimental results obtained have demonstrated the efficacy of this approach in comparison to the state-of-the-art crowd anomaly detection methods.
Samar Mahmoud is a PhD candidate at the University of Greenwich, London. She is currently investigating the possible enhancement of crowd anomaly detection using CGANs and dynamic image representations. She has also created a public abnormal high-density crowd (AHDCrowd) dataset for researchers to train and test crowd anomaly detection methods.