Algorithm for Accurate People Counting in Conference Halls

Abstract

Background: Intelligent conference rooms are crucial to 21st-century enterprises for events. Safety, resource optimization, and event management depend on accurate counting in such contexts. Manual headcounts are effective yet inefficient and error-prone, particularly for big crowds, requiring automatic people counters. Objective: This article introduces and validates a data-driven algorithm to count and track people in an intelligent conference hall. The concept uses IoT infrastructure, low-resolution cameras, and powerful image-processing algorithms to improve security, resource usage, and real-time management choices. Methods: The message-oriented IoT algorithm incorporates motion detection, background subtraction, people counting, and tracking modules. Blob analysis, edge detection, and low-maintenance, low-resolution cameras are used to capture real-world data. Based on real-time data, a decision-making module controls the conference hall's atmosphere. Results: With a 96.5% accuracy rate and 95% confidence interval in real-time individual counts, the algorithm operates with exceptional dependability. Using real-world data and experimental findings, the algorithm has been extensively tested and shown to work in diverse head counting situations. Conclusion: Intelligent conference hall management using the suggested algorithm might revolutionize venue management. The algorithm's accurate, real-time headcounts improve security, resource utilization, and management decisions, making it a promising candidate for intelligent conference hall management and optimization for diverse events and gatherings.

Keywords

• matlab
• algorithm
• people counting
• iot framework
• motion detection
• background subtraction
• tracking
• decision-making module
• low-resolution cameras
• environment control
• actual world data set
• experimental results.