Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis
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ORIGINAL PAPER
Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis Vishal Mandal1,2 · Yaw Adu‑Gyamfi1 Received: 14 August 2020 / Revised: 22 October 2020 / Accepted: 2 November 2020 © Springer Nature Singapore Pte Ltd. 2020
Abstract The rapid advancement in the field of deep learning and high performance computing has highly augmented the scope of video-based vehicle counting system. In this paper, the authors deploy several state-of-the-art object detection and tracking algorithms to detect and track different classes of vehicles in their regions of interest (ROI). The goal of correctly detecting and tracking vehicles’ in their ROI is to obtain an accurate vehicle count. Multiple combinations of object detection models coupled with different tracking systems are applied to access the best vehicle counting framework. The models’ addresses challenges associated to different weather conditions, occlusion and low-light settings and efficiently extracts vehicle information and trajectories through its computationally rich training and feedback cycles. The automatic vehicle counts resulting from all the model combinations are validated and compared against the manually counted ground truths of over 9 h’ traffic video data obtained from the Louisiana Department of Transportation and Development. Experimental results demonstrate that the combination of CenterNet and Deep SORT, and YOLOv4 and Deep SORT produced the best overall counting percentage for all vehicles. Keywords Deep learning · Object detection · Tracking · Vehicle counts
Introduction Accurate estimation of the number of vehicles on the road is an important endeavor in intelligent transportation system (ITS). An effective measure of on-road vehicles can have a plethora of application in transportation sciences including traffic management, signal control and on-street parking (Asha and Narasimhadhan 2018; Khan et al. 2019; Li et al. 2016). Technically, most vehicle counting methods are characterized into either hardware or software-based systems (Lin and Sun 2018). Inductive-loop detectors and piezoelectric sensors are the two most extensively used hardware systems till date. Although they have higher accuracies than * Vishal Mandal [email protected] Yaw Adu‑Gyamfi [email protected] 1
Department of Civil and Environmental Engineering, University of Missouri-Columbia, W1024 Lafferre Hall, Columbia, MO 65211, USA
WSP USA, 211 N Broadway #2800, St. Louis, MO 63102, USA
2
software based systems, they are intrusive and expensive to maintain. On the other hand, software based system thats use video cameras and run on computer vision algorithms present an inexpensive and non-intrusive approach to obtain vehicle counts. Similarly, with increasing computing capabilities and recent successes in object detection and tracking technology, they manifest a tremendous potential to surrogate hardware based systems. Part of the reason to make such a claim is due to the rapid advancement in the field of deep learning
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