Towards a Dedicated Computer Vision Tool set for Crowd Simulation Models

TL;DR

This paper presents a specialized computer vision toolkit designed to initialize and validate crowd simulation models by analyzing crowd flow, identifying key locations, and tracking groups in real-world scenarios.

Contribution

It introduces a novel computer vision tool set that aids in calibrating and validating crowd simulation models with field data, enhancing safety planning.

Findings

Effective crowd flow segmentation and counting

Accurate source and sink location identification

Reliable group detection and tracking

Abstract

As the population of world is increasing, and even more concentrated in urban areas, ensuring public safety is becoming a taunting job for security personnel and crowd managers. Mass events like sports, festivals, concerts, political gatherings attract thousand of people in a constraint environment,therefore adequate safety measures should be adopted. Despite safety measures, crowd disasters still occur frequently. Understanding underlying dynamics and behavior of crowd is becoming areas of interest for most of computer scientists. In recent years, researchers developed several models for understanding crowd dynamics. These models should be properly calibrated and validated by means of data acquired in the field. In this paper, we developed a computer vision tool set that can be helpful not only in initializing the crowd simulation models but can also validate the simulation results. The main features of proposed tool set are: (1) Crowd flow segmentation and crowd counting, (2) Identifying source/sink location for understanding crowd behavior, (3) Group detection and tracking in crowds.