Exploring the Integration of Centroid and Deep Sort Algorithms with YOLOv8 Detector for Passenger Counting in Smart Railway Systems

The proposed Enhanced Passenger Counting System represents a significant advancement in optimizing public transportation. By integrating computer vision technology with sophisticated algorithms like the centroid tracking and DeepSORT, it provides accurate and efficient passenger counting. Leveraging deep learning techniques such as YOLOv8 for object detection ensures robust identification, while DeepSORT enhances tracking accuracy. This approach combines object detection and tracking algorithms to monitor individual movements and accurately count passengers entering and exiting. Experimental results demonstrate a 92.3% accuracy rate in real-time scenarios, showcasing its effectiveness for crowd management in public transportation systems.