Paper ID: 2411.10072

Real-Time AI-Driven People Tracking and Counting Using Overhead Cameras

Ishrath Ahamed, Chamith Dilshan Ranathunga, Dinuka Sandun Udayantha, Benny Kai Kiat Ng, Chau Yuen

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.

Submitted: Nov 15, 2024