Algorithm for Accurate People Counting in Conference Halls

Background: Intelligent conference rooms are crucial to 21st-century enterprises for events. Safety, resource optimisation, 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 capture real-world data. A decision-making module controls the conference hall's atmosphere based on real-time data. Results: The algorithm operates with exceptional dependability with a 96.5% accuracy rate and 95% confidence interval in real-time individual counts. 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 revolutionise venue management. The algorithm is accurate, and real-time headcounts improve security, resource utilisation, and management decisions. This makes it a promising candidate for intelligent conference hall management and optimisation for diverse events and gatherings.