基于运动矢量交点密集度的人群恐慌行为检测

Panic Crowd Behavior Detection Based on Intersection Density of Motion Vector

为了更准确有效的识别人群恐慌行为,本文提出了一种利用视频中人群运动矢量的交点密集度来判断人群恐慌异常的新算法.该算法以LK光流法为基础来提取运动人群的运动矢量信息,接着通过获得的信息求取运动矢量间的两两交叉点,然后运用分块法求得区域交叉点密集度,并以此来识别人群异常.对多个视频进行测试,测试结果表明:该算法能够以较高正确率识别视频中人群的恐慌行为.

In order to identify the panic crowd behavior with a more accurate and effective method, a new scheme is proposed which can utilize the intersection density of motion vector in the video to judge the abnormal panic crowd behavior. This algorithm is based on LK optical flow to extract information of motion vector from moving people, and to obtain the intersection between two motion vectors, then uses divided image blocks to get the intersection density which is the key to identify abnormal crowd. Experiments on several datasets show that this algorithm can identify the panic crowd behavior with high accuracy.