Paper:

# A Survey of Video-Based Crowd Anomaly Detection in Dense Scenes

## Junjie Ma, Yaping Dai, and Kaoru Hirota

School of Automation, Beijing Institute of Technology

5 Zhongguancun South Street, Haidian District, Beijing 100081, China

Population growth has made the probability of incidents at large-scale crowd events higher than ever. In the past decades, automated crowd scene analysis done by computer vision has attracted attention. However, severe occlusions and complex crowd behaviors make such analysis a challenge. As a key aspect of crowd scene analysis, a number of works dealing with dense crowd anomaly detection based on computer vision have been presented. This work is a survey of computer vision techniques for analyzing dense crowd scenes. It covers two aspects: crowd density estimation and abnormal event detection. Some problems and perspectives are discussed at the end.

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