Automatic vehicle counting area creation based on vehicle deep learning detection and DBSCAN

Abstract

Deep learning and high-performance computing have augmented and speed-up the scope of video-based vehicles' massive counting. The automatic vehicle counts result from the detection and tracking of the vehicles in certain areas or Regions of Interest (ROI). In this paper, we propose a technique to create a counting area with different traffic-flow directions based on YOLO and DBSCAN You Only Look Once version five (YOLOv5) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). We compare the performance of the method against the manually counted ground truth. The proposed method showed that it is possible to generate the ROIs (counting areas) according to the traffic flow using deep learning techniques with relatively good accuracy (less than 5 % error). These results are promising but we need to explore the limits of this method with more street-view configurations, time and other detection and tracking algorithms, and in an HPC environment.