Massimo: Public Queue Monitoring and Management using Mass-Spring Model

TL;DR

This paper presents Massimo, a queue management system for public spaces that uses computer vision, machine learning, and deep learning to monitor crowd levels and improve customer experience.

Contribution

It introduces a novel integration of intelligent technologies for real-time queue monitoring and management in public environments.

Findings

Accurate crowd level detection using computer vision.

Effective queue regulation through machine learning.

Enhanced customer satisfaction in public spaces.

Abstract

An efficient system of a queue control and regulation in public spaces is very important in order to avoid the traffic jams and to improve the customer satisfaction. This article offers a detailed road map based on a merger of intelligent systems and creating an efficient systems of queues in public places. Through the utilization of different technologies i.e. computer vision, machine learning algorithms, deep learning our system provide accurate information about the place is crowded or not and the necessary efforts to be taken.