Towards Real Time Vehicle Counting using YOLO-Tiny and Fast Motion Estimation

Real-time vehicle detection, tracking and counting from surveillance cameras is a main part for many applications in smart cities. Usually, this task encounters some problems in practice, like the lack of real-time processing of the videos or the errors in detection and/or tracking. In this paper we propose an approach for real time vehicle counting by using Tiny YOLO for detection and fast motion estimation for tracking. Our application is running in Ubuntu with GPU processing, and the next step is to test it on low-budget devices, as Jetson Nano. Experimental results show that our approach achieves high accuracy at real time speed (33.5 FPS) on real traffic videos.