Detecting Violent and Abnormal Crowd activity using Temporal Analysis of Grey Level Co-occurrence Matrix (GLCM) Based Texture Measures
This addresses the need for faster detection of violence in crowded areas to reduce injuries, but it is incremental as it builds on existing computer vision techniques.
The paper tackled the problem of detecting violent and abnormal crowd activity in video footage by developing an automated method using temporal analysis of GLCM-based texture measures, reporting high ROC scores (e.g., 0.9782 on a private dataset and 0.9956 on UMN) for real-time detection.
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.