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

TL;DR

This paper presents a real-time, texture-based computer vision method using GLCM features to detect violent and abnormal crowd activities, improving detection speed and accuracy in crowded environments.

Contribution

It introduces a novel temporal GLCM-based descriptor and inter-frame uniformity measure for real-time crowd violence detection, demonstrating high accuracy across multiple datasets.

Findings

Achieved ROC scores above 0.94 on multiple datasets.

Proposed method is computationally efficient for real-time use.

Effectively distinguishes violent behavior from normal crowd activities.

Abstract

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.