Capturing the Dynamics of Pedestrian Traffic Using a Machine Vision System

TL;DR

This paper presents a machine vision system that automatically tracks and analyzes pedestrian movement dynamics in various traffic scenarios using overhead video footage, providing quantitative motion data and visualizations.

Contribution

The novel system automatically captures and analyzes pedestrian kinematics from overhead videos, accounting for perspective effects, and visualizes pedestrian behavior in different traffic conditions.

Findings

Accurately estimates pedestrian velocity and acceleration from overhead video.

Provides visual and quantitative analysis of pedestrian behavior.

Works across multiple traffic scenarios.

Abstract

We developed a machine vision system to automatically capture the dynamics of pedestrians under four different traffic scenarios. By considering the overhead view of each pedestrian as a digital object, the system processes the image sequences to track the pedestrians. Considering the perspective effect of the camera lens and the projected area of the hallway at the top-view scene, the distance of each tracked object from its original position to its current position is approximated every video frame. Using the approximated distance and the video frame rate (30 frames per second), the respective velocity and acceleration of each tracked object are later derived. The quantified motion characteristics of the pedestrians are displayed by the system through 2-dimensional graphs of the kinematics of motion. The system also outputs video images of the pedestrians with superimposed markers for tracking. These visual markers were used to visually describe and quantify the behavior of the pedestrians under different traffic scenarios.