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The severity of sustained injury resulting from assault-related violence canbe minimised by reducing detection time. However, it has been shown that humanoperators perform poorly at detecting events found in video footage whenpresented with simultaneous feeds. We utilise computer vision techniques todevelop an automated method of abnormal crowd detection that can aid a humanoperator in the detection of violent behaviour. We observed that behaviour incity centre environments often occur in crowded areas, resulting in individualactions being occluded by other crowd members. We propose a real-timedescriptor that models crowd dynamics by encoding changes in crowd textureusing temporal summaries of Grey Level Co-Occurrence Matrix (GLCM) features. Weintroduce a measure of inter-frame uniformity (IFU) and demonstrate that theappearance of violent behaviour changes in a less uniform manner when comparedto other types of crowd behaviour. Our proposed method is computationally cheapand offers real-time description. Evaluating our method using a privately heldCCTV dataset and the publicly available Violent Flows, UCF Web Abnormality, andUMN Abnormal Crowd datasets, we report a receiver operating characteristicscore of 0.9782, 0.9403, 0.8218 and 0.9956 respectively.

Detecting Violent and Abnormal Crowd activity using Temporal Analysis of Grey Level Co-occurrence Matrix (GLCM) Based Texture Measures