结合光流法与信息熵的人群突发事件检测与判断

在公园、学校、购物中心等人流密集的地方，如果发生火灾、地震或犯罪案件，易导致类似踩踏的安全事故。需要掌握区域内人群是混乱还是有序疏散的信息，从而尽快采取有效的应对措施，将人员伤亡降至最低。 提出了一种结合光流法与信息熵的人群突发事件检测与判断算法。通过LK光流算法从视频中提取运动物体的特征点，并得到特征点的位置信息；根据位置信息计算速度和加速度，分析运动强度，检测突发事件；由各速度矢量在方向上的分布得到概率，计算信息熵，由信息熵来判断视频中人群的状态是混乱还是有序。与同类算法相比，本算法能在准确检测突发事件的同时判断人群状态，适用于多种不同场景。

Detection and judgment of crowd emergencies based on optical flow and information entropy

In parks, schools, shopping centers and other crowded places，security incidents are likely to happen in cases of the earthquake, fire and similar accidents. Identifying people's state is of great significance for the arrangement of rescue resources to reduce the casualties. To detect and judge the potential crowd emergencies, this paper proposes an effective algorithm, which is based on optical flow and information entropy of the surveillance video. By adopting LK optical flow algorithm, we extract the feature points of moving objects in the video, thereby obtaining their location, speed and acceleration information. According to acceleration, we can determine whether there are crowd emergencies.Besides, the entropy is calculated through distribution of velocity vector to identify if there is a chaos happening in the video. Compared with the other methods, this algorithm can accurately detect the crowd emergencies, and analyze the state of the crowd. The algorithm is suitable to deal with environment with different backgrounds.