# Estimation of crowd density applying wavelet transform and machine learning

## TL;DR

This paper introduces a novel method combining wavelet transform and machine learning to estimate crowd density from body-rotational angular velocity data, simplifying crowd monitoring for safety and comfort.

## Contribution

It presents a new approach that accurately predicts crowd density using angular velocity data, reducing reliance on raw velocity measurements.

## Key findings

- Angular velocity correlates with crowd density.
- Prediction accuracy is comparable between angular velocity and raw velocity.
- Method enhances safety management in crowded areas.

## Abstract

We conducted a simple experiment in which one pedestrian passed through a crowded area and measured the body-rotational angular velocity with commercial tablets. Then, we developed a new method for predicting crowd density by applying the continuous wavelet transform and machine learning to the data obtained in the experiment. We found that the accuracy of prediction using angular velocity data was as high as that using raw velocity data. Therefore, we concluded that angular velocity has relationship with crowd density and we could estimate crowd density by angular velocity. Our research will contribute to management of safety and comfort of pedestrians by developing an easy way to measure crowd density.