Effective Crowd Anomaly Detection Through Spatio-temporal Texture Analysis

Yu Hao, Zhijie Xu, Ying Liu, Jing Wang, Jiulun Fan

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)27-39
Number of pages13
JournalInternational Journal of Automation and Computing
Issue number1
Early online date27 Sep 2018
Publication statusPublished - 1 Feb 2019


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