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Extracting Spatio-temporal Texture Signatures for Crowd Abnormality Detection

Hao, Yu, Wang, Jing, Liu, Ying, Xu, Zhijie and Fan, Jiulun (2017) Extracting Spatio-temporal Texture Signatures for Crowd Abnormality Detection. Proceedings of the 23rd International Conference on Automation & Computing, (University of Huddersfield, 7-8 September 2017). ISSN 9780701702601

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In order to achieve automatic prediction and warning of hazardous crowd behaviors, a Spatio-Temporal Volume (STV) analysis method is proposed in this research to detect crowd abnormality recorded in CCTV streams. The method starts from building STV models using video data. STV slices – called Spatio-Temporal Textures (STT) - can then be analyzed to detect crowded regions. After calculating the Gray Level Co-occurrence Matrix (GLCM) among those regions, abnormal crowd behavior can be identified, including panic behaviors and other behavioral patterns. In this research, the proposed STT signatures have been defined and experimented on benchmarking video databases. The proposed algorithm has shown a promising accuracy and efficiency for detecting crowd-based abnormal behaviors. It has been proved that the STT signatures are suitable descriptors for detecting certain crowd events, which provide an encouraging direction for real-time surveillance and video retrieval applications.

Item Type: Article
AuthorLiu, Yingly_yolanda@sina.comUNSPECIFIED
Additional Information: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Crowd abnormality; Spatio-Temporal Volume; STT Signature
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Schools: School of Computing and Engineering
Related URLs:
Depositing User: Zhijie Xu
Date Deposited: 10 Oct 2017 08:46
Last Modified: 28 Aug 2021 15:31


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