Journal Article

A nighttime highway traffic flow monitoring system using vision-based vehicle detection and tracking

Azimjonov, Jahongir, Kim, Taehong, Özmen, Ahmet

2023

Overview

Accurately estimated highway traffic flow info plays a decisive role in dynamic and real-time road management, planning, and preventing frequent/recurring traffic jams, traffic rule violations, and chain/fatal traffic accidents. Traffic flow information is extracted by processing raw camera images via vehicle detection and tracking algorithms. Object detectors including the Yolo, single-shot detector, and EfficientNet algorithms are used for vehicle detection; however, You only look once version 5 (Yolov5) has a clear advantage in terms of real-time performance. Due to this reason, the pre-trained Yolov5 models were utilized in the vehicle detection part, and in the vehicle tracking module, a novel tracker algorithm was developed using vehicle detection features. The performance of the proposed approach was measured by comparing it to the Kalman filter-based tracker. The evaluation results show that the proposed tracking approach outperformed the Kalman filter-based tracker with 5.82% (Buses), 2.24% (Cars), 36.50% (Trucks), and overall 2.58% better traffic counting accuracy for the 12 nighttime case study videos captured from the highways with different horizontal and vertical angle-of-views.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Algorithms / Artificial Intelligence / Computational Intelligence / Control / Data Analytics and Machine Learning / Engineering / Kalman filters / Localization / Machine learning / Mathematical Logic and Foundations / Mechatronics / Monitoring systems / Neural networks / Night / Object recognition / Real time / Roads & highways / Robotics / Tracking / Traffic accidents / Traffic congestion / Traffic flow / Traffic information / Traffic jams / Urban areas / Vehicles