  The severity of sustained injury resulting from assault-related violence can
be minimised by reducing detection time. However, it has been shown that human
operators perform poorly at detecting events found in video footage when
presented with simultaneous feeds. We utilise computer vision techniques to
develop an automated method of abnormal crowd detection that can aid a human
operator in the detection of violent behaviour. We observed that behaviour in
city centre environments often occur in crowded areas, resulting in individual
actions being occluded by other crowd members. We propose a real-time
descriptor that models crowd dynamics by encoding changes in crowd texture
using temporal summaries of Grey Level Co-Occurrence Matrix (GLCM) features. We
introduce a measure of inter-frame uniformity (IFU) and demonstrate that the
appearance of violent behaviour changes in a less uniform manner when compared
to other types of crowd behaviour. Our proposed method is computationally cheap
and offers real-time description. Evaluating our method using a privately held
CCTV dataset and the publicly available Violent Flows, UCF Web Abnormality, and
UMN Abnormal Crowd datasets, we report a receiver operating characteristic
score of 0.9782, 0.9403, 0.8218 and 0.9956 respectively.
