A Review on Video Anomaly Detection Datasets

Document Type : Review Article

Authors

1 Electrical Engineering Department, Faculty of Engineering, Suez Canal University, Ismailia, Egypt

2 Department of Electrical Engineering, Suez Canal University, Egypt

3 Electrical Engineering Department, Faculty of Engineering, Port Said University, Port Said, Egypt.

4 Electrical Engineering Department, Faculty of Engineering, Suez Canal University, Ismailia, Egypt.

Abstract

In recent years, Video Anomaly Detection (VAD) has received a lot of attention and has become a popular research topic. This is due to their immense potential in a variety of fields, including healthcare monitoring, surveillance/crowd analysis, sports, Ambient Assistive Living (AAL), event analysis, and security. Manually detecting and analysing improper behavior was a hard process, particularly in real-time scenarios, resulting in a high demand for smart surveillance systems. Moreover, the availability of data plays a vital role in training and evaluating models. Datasets in VAD are typically composed of sequences of frames or videos, some of which depict normal activities and others that depict anomalous or unusual events. These datasets provide a rich resource that encapsulates everyday routine actions alongside irregular or unusual events, fostering the development and assessment of robust anomaly detection models. This paper provides an extensive review of the most popular and recent datasets in VAD including an extensive comparison between them.

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