The Institute of Electrical Engineering, YanShan University, Qinhuangdao 066004, China.
School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel). 2018 Feb 1;18(2):423. doi: 10.3390/s18020423.
The change of crowd energy is a fundamental measurement for describing a crowd behavior. In this paper, we present a crowd abnormal detection method based on the change of energy-level distribution. The method can not only reduce the camera perspective effect, but also detect crowd abnormal behavior in time. Pixels in the image are treated as particles, and the optical flow method is adopted to extract the velocities of particles. The qualities of different particles are distributed as different value according to the distance between the particle and the camera to reduce the camera perspective effect. Then a crowd motion segmentation method based on flow field texture representation is utilized to extract the motion foreground, and a linear interpolation calculation is applied to pedestrian's foreground area to determine their distance to the camera. This contributes to the calculation of the particle qualities in different locations. Finally, the crowd behavior is analyzed according to the change of the consistency, entropy and contrast of the three descriptors for co-occurrence matrix. By calculating a threshold, the timestamp when the crowd abnormal happens is determined. In this paper, multiple sets of videos from three different scenes in UMN dataset are employed in the experiment. The results show that the proposed method is effective in characterizing anomalies in videos.
人群能量的变化是描述人群行为的基本度量。在本文中，我们提出了一种基于能量分布变化的人群异常检测方法。该方法不仅可以减少摄像机视角的影响，还可以及时检测人群的异常行为。将图像中的像素视为粒子，并采用光流法提取粒子的速度。根据粒子与摄像机的距离，将不同粒子的质量分配为不同的值，以减少摄像机视角的影响。然后，利用基于流场纹理表示的人群运动分割方法提取运动前景，并对行人的前景区域进行线性插值计算，以确定其与摄像机的距离。这有助于计算不同位置的粒子质量。最后，根据共生矩阵的一致性、熵和对比度三个描述符的变化来分析人群行为。通过计算一个阈值，确定人群异常发生的时间戳。本文在实验中使用了来自 UMN 数据集三个不同场景的多组视频。实验结果表明，所提出的方法能够有效地描述视频中的异常情况。
