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Open AccessArticle

Energy Level-Based Abnormal Crowd Behavior Detection

1
The Institute of Electrical Engineering, YanShan University, Qinhuangdao 066004, China
2
School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
3
LargeV Instrument Corporation Limited, Beijing 100084, China
4
School of Creative Technologies, University of Portsmouth, Portsmouth PO1 2DJ, UK
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(2), 423; https://doi.org/10.3390/s18020423
Received: 5 January 2018 / Revised: 23 January 2018 / Accepted: 29 January 2018 / Published: 1 February 2018
(This article belongs to the Section Intelligent Sensors)
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. View Full-Text
Keywords: crowd abnormal detection; energy-level; flow field visualization; co-occurrence matrix crowd abnormal detection; energy-level; flow field visualization; co-occurrence matrix
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Zhang, X.; Zhang, Q.; Hu, S.; Guo, C.; Yu, H. Energy Level-Based Abnormal Crowd Behavior Detection. Sensors 2018, 18, 423.

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