Vehicle Counting Using Detecting-Tracking Combinations: A Comparative Analysis

In light of the rapid progress in building smart cities and smart traffic systems, the need for an accurate and real-time counting vehicles system has become a very urgent need. Finding a robust and accurate counting system is a challenge, as the system must detect, classify and track multi vehicles in complex and dynamic scene situations, different models and classes, and various traffic densities. Several hardware and software systems have emerged for this purpose and their results have varied. In recent years, and due to the great growth in computational capacities and deep learning techniques, deep learning based vehicle counting systems have delivered an impressive performance at low costs. In this study, several state-of-the-art detection and tracking algorithms are studied and combined with each other to render different models. These models are applied in automatic vehicle counting frameworks in traffic videos to assess how accurate are their results against the ground truth. Experiments on these models present the existing challenges that hinder their ability to extract the distinctive object features and thus undermine their efficiency such as problems of occlusion, large scale objects detection, illumination, and various weather conditions. The study revealed that the detectors coupled with the Deep Sort tracker, such as YOLOv4, Detectron2 and CenterNet, achieved the best results compared to the rest of the models.