Automated Lane Detection in Crowds using Proximity Graphs

TL;DR

This paper introduces a novel automated method for detecting lane formation patterns in crowds using proximity graphs derived from on-body sensors, contributing to understanding collective human behaviors.

Contribution

It proposes a formal definition of lanes, a probabilistic model for lane movement, and an automated detection method for crowd pattern analysis.

Findings

Method detects lanes of various shapes and sizes

Preliminary results show promising pattern recognition capabilities

Approach advances crowd behavior analysis using proximity graphs

Abstract

Studying the behavior of crowds is vital for understanding and predicting human interactions in public areas. Research has shown that, under certain conditions, large groups of people can form collective behavior patterns: local interactions between individuals results in global movements patterns. To detect these patterns in a crowd, we assume each person is carrying an on-body device that acts a local proximity sensor, e.g., smartphone or bluetooth badge, and represent the texture of the crowd as a proximity graph. Our goal is extract information about crowds from these proximity graphs. In this work, we focus on one particular type of pattern: lane formation. We present a formal definition of a lane, proposed a simple probabilistic model that simulates lanes moving through a stationary crowd, and present an automated lane-detection method. Our preliminary results show that our method is able to detect lanes of different shapes and sizes. We see our work as an initial step towards rich pattern recognition using proximity graphs.