Paper Info
Title
Energy Level-Based Abnormal Crowd Behavior Detection.
Abstract
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.
Year
DOI
Venue
2018
10.3390/s18020423
SENSORS
Keywords
Field
DocType
crowd abnormal detection,energy-level,flow field visualization,co-occurrence matrix
Computer vision,Co-occurrence matrix,Matrix (mathematics),Segmentation,Electronic engineering,Timestamp,Artificial intelligence,Pixel,Engineering,Linear interpolation,Optical flow,Crowd psychology
Journal
Volume
Issue
Citations 
18
2.0
2
PageRank 
References 
Authors
0.38
24
5
Name
Order
Citations
PageRank
Xuguang Zhang17916.74
Qian Zhang229043.11
Shuo Hu320.72
Chunsheng Guo474.59
Hui Yu512821.50