Vehicle Counting on Vietnamese Street

Object counting is the process of determining the count of objects in images using computer vision techniques. In this paper, we employ several state-of-the-art object detection and tracking algorithms to solve the object counting problem in image regions of interest (ROI) on Vietnamese streets. Specifically, we propose video-based methods for counting vehicles in various weather conditions and low-light environments, a new dataset for Vietnamese streets, and retrain the scratch model on the new dataset. A video is processed in three phases, including object detection with YOLO (You Only Look Once), tracking with StrongSORT, and vehicle counting in ROI. The experimental analysis of real-world video footage demonstrates that the proposed method can accurately detect, monitor, and count vehicles. In addition, by using our collected dataset, the proposed method performs significantly better than the pretrained YOLO model.