Crowd abnormal behavior detection based on optical flow and track

被引：0
|
作者：
Wang H.-Y. [1 ,2 ,3 ]
Zhou M.-X. [1 ]
机构：
[1] College of Information Engineering, Dalian University, Dalian
[2] School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou
[3] Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2020年 / 50卷 / 06期
关键词：
Computer application; Crowd abnormal behavior; Synthetic optical flow feature descriptor(SOFD); Track; Two stream convolutional neural networks(TS-CNN);
D O I：
10.13229/j.cnki.jdxbgxb20190665
中图分类号：
学科分类号：
摘要：
Focusing on the problem of low detection performance of crowd abnormal behavior conducted by complex background and occlusion as well as other factors, a crowd abnormal behavior detection method is proposed based on synthetic optical flow feature descriptor (SOFD) and trajectory. First, the velocity, acceleration, direction and energy of crowd motion are calculated according to the change of crowd optical flow field. Then, a new space-time feature descriptor, i.e., SOFD, is constructed based on the above characteristics. Third, the Kanade-Lucas-Tomasi (KLT) tracking algorithm is employed to obtain the single frame of crowd motion trajectory. Finally, the two stream convolution neural network (TS-CNN) is depicted with the abovementioned characteristics to detect the crowd abnormal behavior. Compared with the existing state-of-the-arts algorithms, simulation results show that the proposed method has higher accuracy,better robustness and wide application range in abnormal behavior detection under complex environments. © 2020, Jilin University Press. All right reserved.
引用
收藏
页码：2229 / 2237
页数：8
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