This paper presents an approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior. This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence. First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm. A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language. The group events recognition approach is successfully validated on 4 camera views from 3 datasets: an airport, a subway, a shopping center corridor and an entrance hall.

本文提出了一种在视频监控应用中检测和跟踪人群以及自动识别他们行为的方法，该方法通过维持空间和时间上的协同性来追踪移动在一起的个人，其中人员的轨迹经过时间窗口分析并使用 Mean-Shift 算法聚类，协同值描述了一组人群的描述质量，同时提出了一个正式的事件描述语言，该方法在机场、地铁、购物中心走廊和入口大厅的3个数据集中的4个摄像头视角上成功验证了人群事件的识别。

应用于群体行为识别的视频理解通用框架