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

  • Data analytics and machine learning
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Abstract

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.

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Funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2022R1I1A3072355) and by the Scientific and Technological Research Council of Turkey under the Grant No. 119E077 and Title: “Development of a Customized Traffic Planning System for Sakarya City by Processing Multiple Camera Images with Convolutional Neural Networks (CNN) and Machine Learning Techniques”.

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Authors and Affiliations

  1. School of Information and Communication Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, 28644, Cheongju, South Korea

    Jahongir Azimjonov & Taehong Kim

  2. Department of Software Engineering, Sakarya University, Esentepe Campus, 54187, Sakarya, Turkey

    Ahmet Özmen

Authors
  1. Jahongir Azimjonov
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  2. Ahmet Özmen
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  3. Taehong Kim
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Correspondence to Taehong Kim.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. All authors of this research paper have directly participated in the planning, execution, or analysis of this study.

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Azimjonov, J., Özmen, A. & Kim, T. A nighttime highway traffic flow monitoring system using vision-based vehicle detection and tracking. Soft Comput 27, 13843–13859 (2023). https://doi.org/10.1007/s00500-023-08860-z

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