Effective Crowd Anomaly Detection Through Spatio-temporal Texture Analysis

doi:10.1007/s11633-018-1141-z

CASIA OpenIR > 学术期刊 > Machine Intelligence Research

	Effective Crowd Anomaly Detection Through Spatio-temporal Texture Analysis
	Yu Hao1,2 ; Zhi-Jie Xu 2; Ying Liu 1; Jing Wang3 ; Jiu-Lun Fan 1
发表期刊	International Journal of Automation and Computing
ISSN	1476-8186
	2019
卷号	16 期号:1 页码:27-39
摘要	Abnormal crowd behaviors in high density situations can pose great danger to public safety. Despite the extensive installation of closed-circuit television (CCTV) cameras, it is still difficult to achieve real-time alerts and automated responses from current systems. Two major breakthroughs have been reported in this research. Firstly, a spatial-temporal texture extraction algorithm is developed. This algorithm is able to effectively extract video textures with abundant crowd motion details. It is through adopting Gabor-filtered textures with the highest information entropy values. Secondly, a novel scheme for defining crowd motion patterns (signatures) is devised to identify abnormal behaviors in the crowd by employing an enhanced gray level co-occurrence matrix model. In the experiments, various classic classifiers are utilized to benchmark the performance of the proposed method. The results obtained exhibit detection and accuracy rates which are, overall, superior to other techniques.
关键词	Crowd behavior spatial-temporal texture gray level co-occurrence matrix information entropy.
DOI	10.1007/s11633-018-1141-z
引用统计	被引频次：33[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42319
专题	学术期刊_Machine Intelligence Research
作者单位	1.School of Computer Science and Technology, Xi′an University of Posts and Telecommunications, Xi′an 710121, China 2.School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK 3. 3. Faculty of Arts Computing Engineering and Sciences, Sheffield Hallam University, Sheffield S1 1WB, UK
推荐引用方式 GB/T 7714	Yu Hao,Zhi-Jie Xu,Ying Liu,et al. Effective Crowd Anomaly Detection Through Spatio-temporal Texture Analysis[J]. International Journal of Automation and Computing,2019,16(1):27-39.
APA	Yu Hao,Zhi-Jie Xu,Ying Liu,Jing Wang,&Jiu-Lun Fan.(2019).Effective Crowd Anomaly Detection Through Spatio-temporal Texture Analysis.International Journal of Automation and Computing,16(1),27-39.
MLA	Yu Hao,et al."Effective Crowd Anomaly Detection Through Spatio-temporal Texture Analysis".International Journal of Automation and Computing 16.1(2019):27-39.

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