Automated scene analysis has been a topic of great interest in computer
vision and cognitive science. Recently, with the growth of crowd phenomena in
the real world, crowded scene analysis has attracted much attention. However,
the visual occlusions and ambiguities in crowded scenes, as well as the complex
behaviors and scene semantics, make the analysis a challenging task. In the
past few years, an increasing number of works on crowded scene analysis have
been reported, covering different aspects including crowd motion pattern
learning, crowd behavior and activity analysis, and anomaly detection in
crowds. This paper surveys the state-of-the-art techniques on this topic. We
first provide the background knowledge and the available features related to
crowded scenes. Then, existing models, popular algorithms, evaluation
protocols, as well as system performance are provided corresponding to
different aspects of crowded scene analysis. We also outline the available
datasets for performance evaluation. Finally, some research problems and
promising future directions are presented with discussions.

本文综述了近年来有关拥挤场景分析的技术现状，包括群体动作模式学习、群体行为和活动分析以及异常检测等不同方面，并概述了性能评价、研究问题和未来发展方向。