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

Abhijeet Kumar, Unnati Singh, Rajdeep Chatterjee, Tathagata Bandyopadhyay

TL;DR

A detailed road map based on a merger of intelligent systems and creating an efficient systems of queues in public places is offered.

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.

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

TL;DR

A detailed road map based on a merger of intelligent systems and creating an efficient systems of queues in public places is offered.

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.

Paper Structure

This paper contains 28 sections, 17 equations, 6 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Social Distancing and Crowd Management
  4. Real-Time Human Detection and Counting System Using Deep Learning
  5. Background Concepts
  6. Object Detection Models
  7. Regression Techniques
  8. Confidence Intervals
  9. Physics-Based Outlier Detection
  10. Derivation of Spring Force
  11. Color Mapping and Visualization
  12. Proposed Solution
  13. Heap Area Coordinate Detection Methods
  14. Finding the Optimal Line That Needs to Be Followed by Everyone
  15. Finding Outliers That Are Not Within the 95% Confidence Interval of the Predicted Line
  16. ...and 13 more sections

Figures (6)

  • Figure 1: Example of outlier detection using the 95% confidence interval method.
  • Figure 2: Graphical representation of Confidence Interval
  • Figure 3: Visualization of spring forces acting between individuals.
  • Figure 4: Visualization of the components of spring forces acting between individuals.
  • Figure 5: Visualization of force magnitudes using color mapping.
  • ...and 1 more figures