Monitoring crowd density is a crucial task for ensuring safety and preventing overcrowding-related issues. The traditional methods for monitoring crowds involve manual observation and camera surveillance, which are time-consuming and require continuous monitoring. This paper proposes a hybrid approach for crowd detection using Raspberry Pi, incorporating wireless device detection, Bluetooth scanning, infrared sensing, and computer vision. The system estimates the crowd density based on wireless device detection and verifies the presence of people through OpenCV-based human detection. The infrared sensor is used to improve the accuracy of the system by tracking entry and exit movements. The hybrid approach is an improvement over traditional methods, reducing the limitations associated with each method. The paper also discusses different approaches to crowd detection, highlighting the advantages and limitations of these methods, and the benefits of a hybrid approach for real-time applications.
Tejaswini et al. (Thu,) studied this question.