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

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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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References

  • Azimjonov J, Özmen A (2021) A real-time vehicle detection and a novel vehicle tracking systems for estimating and monitoring traffic flow on highways. Adv Eng Inform 50:101393

    Article  Google Scholar 

  • Azimjonov J, Özmen A (2022) Vision-based vehicle tracking on highway traffic using bounding-box features to extract statistical information. Comput Electr Eng 97:107560

    Article  Google Scholar 

  • Badino H, Franke U, Mester R (2007) Free space computation using stochastic occupancy grids and dynamic. In: Programming, proceedings of the international conference computer vision, workshop dynamical vision

  • Cabido R, Montemayor AS, Pantrigo JJ (2012) High performance memetic algorithm particle filter for multiple object tracking on modern GPUs. Soft Comput 16:217–230

    Article  Google Scholar 

  • Chauhan NK, Singh K (2018) A review on conventional machine learning vs deep learning. In: 2018 international conference on computing, power and communication technologies (GUCON), pp 347–352

  • Datondji SRE, Dupuis Y, Subirats P, Vasseur P (2016) A survey of vision-based traffic monitoring of road intersections. IEEE Trans Intell Transp Syst 17(10):2681–2698

    Article  Google Scholar 

  • Erbs F, Barth A, Franke U (2011) Moving vehicle detection by optimal segmentation of the dynamic stixel world. In: 2011 IEEE intelligent vehicles symposium (IV), pp 951–956

  • Feng X, Jiang Y, Yang X, Du M, Li X (2019) Computer vision algorithms and hardware implementations: a survey. Integration 69:309–320

    Article  Google Scholar 

  • Fernández-Sanjurjo M, Bosquet B, Mucientes M, Brea VM (2019) Real-time visual detection and tracking system for traffic monitoring. Eng Appl Artif Intell 85:410–420

    Article  Google Scholar 

  • Franke U, Rabe C, Badino H, Gehrig S (2005) 6d-vision: fusion of stereo and motion for robust environment perception. In: Kropatsch WG, Sablatnig R, Hanbury A (eds) Pattern recognition. Springer, Berlin, pp 216–223

    Chapter  Google Scholar 

  • Girshick R (2015) Fast R-CNN. In: 2015 IEEE international conference on computer vision (ICCV), pp 1440–1448

  • Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE conference on computer vision and pattern recognition, pp 580–587

  • Jeon D, Kim D-H, Ha Y-G, Tyan V (2016) Image processing acceleration for intelligent unmanned aerial vehicle on mobile GPU. Soft Comput 20:1713–1720

    Article  Google Scholar 

  • Kanagamalliga S, Vasuki S (2018) Contour-based object tracking in video scenes through optical flow and Gabor features. Optik 157:787–797

    Article  Google Scholar 

  • Kavukcuoglu K, Sermanet P, lan Boureau Y, Gregor K, Mathieu M, Cun YL (2010) Learning convolutional feature hierarchies for visual recognition. In: Lafferty JD, Williams CKI, Shawe-Taylor J, Zemel RS, Culotta A (eds) Advances in neural information processing systems 23. Curran Associates, Inc, pp 1090–1098

  • Khalkhali MB, Vahedian A, Yazdi HS (2020) Vehicle tracking with Kalman filter using online situation assessment. Robot. Auton. Syst. 131:103596

    Article  Google Scholar 

  • Khan S, Ali H, Ullah Z, Bulbul MF (2018) An intelligent monitoring system of vehicles on highway traffic. In: 2018 12th international conference on open source systems and technologies (ICOSST), pp 71–75

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436-444

    Article  Google Scholar 

  • Liu P, Wang G, Yu Z, Guo X, Lu W (2019) Vehicle tracking based on shape information and inter-frame motion vector. Comput Electr Eng 78:22–31

    Article  Google Scholar 

  • Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: single shot multibox detector. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision—ECCV 2016. Springer, Cham, pp 21–37

  • Lu S, Wang Y, Song H (2020) A high accurate vehicle speed estimation method. Soft Comput 24:1283–1291

    Article  Google Scholar 

  • Luque-Baena RM, López-Rubio E, Domínguez E, Palomo EJ, Jerez JM (2015) A self-organizing map to improve vehicle detection in flow monitoring systems. Soft Comput 19:2499–2509

    Article  Google Scholar 

  • Mandellos NA, Keramitsoglou I, Kiranoudis CT (2011) A background subtraction algorithm for detecting and tracking vehicles. Expert Syst Appl 38(3):1619–1631

    Article  Google Scholar 

  • Nguyen B, Brilakis I (2018) Real-time validation of vision-based over-height vehicle detection system. Adv Eng Inform 38:67–80

    Article  Google Scholar 

  • Pillai MS, Chaudhary G, Khari M, Crespo RG (2021) Real-time image enhancement for an automatic automobile accident detection through CCTV using deep learning. Soft Comput 25:11929

    Article  Google Scholar 

  • Rathore MM, Son H, Ahmad A, Paul A (2018) Real-time video processing for traffic control in smart city using Hadoop ecosystem with GPUs. Soft Comput 22:1533–1544

    Article  Google Scholar 

  • Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 779–788

  • Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 6517–6525

  • Redmon J, Farhadi A (2018) Yolov3: an incremental improvement, pp 1–6. arXiv preprint arXiv:1804.02767

  • Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149

    Article  Google Scholar 

  • Sivaraman S, Trivedi MM (2013) Looking at vehicles on the road: a survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Trans Intell Transp Syst 14(4):1773–1795

    Article  Google Scholar 

  • Song H, Wang X, Hua C, Wang W, Guan Q, Zhang Z (2018) Vehicle trajectory clustering based on 3d information via a coarse-to-fine strategy. Soft Comput 22:1433–1444

    Article  Google Scholar 

  • Song D, Tharmarasa R, Florea MC, Duclos-Hindie N, Fernando XN, Kirubarajan T (2019) Multi-vehicle tracking with microscopic traffic flow model-based particle filtering. Automatica 105:28–35

    Article  MathSciNet  MATH  Google Scholar 

  • Sudha D, Priyadarshini J (2020) An intelligent multiple vehicle detection and tracking using modified vibe algorithm and deep learning algorithm. Soft Comput 24:17417–17429

    Article  Google Scholar 

  • Xiao X, Sun Z, Shen W (2020) A Kalman filter algorithm for identifying track irregularities of railway bridges using vehicle dynamic responses. Mech Syst Signal Process 138:106582

    Article  Google Scholar 

  • Yang Z, Pun-Cheng LS (2018) Vehicle detection in intelligent transportation systems and its applications under varying environments: a review. Image Vis Comput 69:143–154

    Article  Google Scholar 

  • Yang T, Cappelle C, Ruichek Y, Bagdouri ME (2019) Online multi-object tracking combining optical flow and compressive tracking in Markov decision process. J Vis Commun Image Represent 58:178–186

  • Zhao Z, Zheng P, Xu S, Wu X (2019) Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst 30(11):3212-3232

    Article  Google Scholar 

  • Zhu Y, Comaniciu D, Pellkofer M, Koehler T (2006) Reliable detection of overtaking vehicles using robust information fusion. IEEE Trans Intell Transp Syst 7(4):401–414

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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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