VNAnomaly: A novel Vietnam surveillance video dataset for anomaly detection

Surveillance systems have long been considered as an effective tool to capture various realistic abnormal actions or events in various domains such as traffic management or security. With the smart city development, thousand of installed surveillance cameras have played a vital role in detection and prevention of dangerous events. However, there is a lack of anomaly datasets for developing automatic anomaly detection systems in Vietnam. In this study, we introduce a new dataset named VNAnomaly for anomaly detection in Vietnam. Moreover, we also conduct a thorough evaluation of current state-of-the-art for unsupervised anomaly detection methods based on deep architectures including MLEP, Future frame prediction, MNAD, and MNAD with modified inference on benchmark datasets and our dataset. Experimental results indicate that the proposed method almost always outperforms the competitors and achieves the best performance in terms of Area Under the Curve (AUC) score at 61.14%.