Development of Robust Vehicle Classification and Tracking for Traffic Analysis

Abstract

The Japanese government plans to replace the manual survey method of counting vehicles with CCTV images in order to obtain traffic volume by vehicle types automatically. The current method of manually checking vehicles and counting them as small or large vehicles is time-consuming, and there is a shortage of surveyors. Automating this process will allow for more accurate and constant monitoring of traffic volume. Existing research can be found in developing vehicle detection and tracking models using machine learning. However, these models have not yet achieved the accuracy required for practical use. This paper aims to develop a vehicle classification and tracking model with high accuracy for 7 classes of vehicle types. YOLOv5 is used for vehicle detection and classification, along with DeepSORT for vehicle tracking. The results showed most of the classes reached 95% accuracy both for classification and tracking.