Real-Time AI-Driven People Tracking and Counting Using Overhead Cameras
It addresses the need for precise occupant counts in smart buildings and transportation systems, especially during emergencies, with an incremental improvement over existing methods.
This study tackled the problem of accurate people counting in crowded environments using overhead cameras, achieving 97% accuracy in real-time with a frame rate of 20-27 FPS on a low-power edge computer.
Accurate people counting in smart buildings and intelligent transportation systems is crucial for energy management, safety protocols, and resource allocation. This is especially critical during emergencies, where precise occupant counts are vital for safe evacuation. Existing methods struggle with large crowds, often losing accuracy with even a few additional people. To address this limitation, this study proposes a novel approach combining a new object tracking algorithm, a novel counting algorithm, and a fine-tuned object detection model. This method achieves 97% accuracy in real-time people counting with a frame rate of 20-27 FPS on a low-power edge computer.