Vision-based Conflict Detection within Crowds based on High-Resolution Human Pose Estimation for Smart and Safe Airport

TL;DR

This paper presents a machine learning approach using high-resolution human pose estimation to detect conflicts in crowds at airports, aiming to enhance security and safety through improved surveillance.

Contribution

It introduces a novel model combining HRNet and classifiers, notably SVM, for conflict detection in crowded airport environments, with promising accuracy.

Findings

SVM achieved 94.37% precision in conflict classification.

The model effectively detects conflicts but struggles with ambiguous behaviors.

Potential for deployment with further improvements and training.

Abstract

Future airports are becoming more complex and congested with the increasing number of travellers. While the airports are more likely to become hotspots for potential conflicts to break out which can cause serious delays to flights and several safety issues. An intelligent algorithm which renders security surveillance more effective in detecting conflicts would bring many benefits to the passengers in terms of their safety, finance, and travelling efficiency. This paper details the development of a machine learning model to classify conflicting behaviour in a crowd. HRNet is used to segment the images and then two approaches are taken to classify the poses of people in the frame via multiple classifiers. Among them, it was found that the support vector machine (SVM) achieved the most performant achieving precision of 94.37%. Where the model falls short is against ambiguous behaviour such as a hug or losing track of a subject in the frame. The resulting model has potential for deployment within an airport if improvements are made to cope with the vast number of potential passengers in view as well as training against further ambiguous behaviours which will arise in an airport setting. In turn, will provide the capability to enhance security surveillance and improve airport safety.