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An improved YOLO-based road traffic monitoring system

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Abstract

The growing population in large cities is creating traffic management issues. The metropolis road network management also requires constant monitoring, timely expansion, and modernization. In order to handle road traffic issues, an intelligent traffic management solution is required. Intelligent monitoring of traffic involves the detection and tracking of vehicles on roads and highways. There are various sensors for collecting motion information, such as transport video detectors, microwave radars, infrared sensors, ultrasonic sensors, passive acoustic sensors, and others. In this paper, we present an intelligent video surveillance-based vehicle tracking system. The proposed system uses a combination of the neural network, image-based tracking, and You Only Look Once (YOLOv3) to track vehicles. We train the proposed system with different datasets. Moreover, we use real video sequences of road traffic to test the performance of the proposed system. The evaluation outcomes showed that the proposed system can detect, track, and count the vehicles with acceptable results in changing scenarios.

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References

  1. Zhu Y, Wang J, Lu H (2008) A study on urban traffic congestion dynamic predict method based on advanced fuzzy clustering model. In: Proceedings of the 2008 international conference on computational intelligence and security, vol 2, pp 96–100. IEEE.

  2. De Oliveira MB, Neto ADA (2013) Optimization of traffic lights timing based on multiple neural networks. In: Proceedings of the 2013 IEEE 25th international conference on tools with artificial intelligence, pp 825–832. IEEE.

  3. Lee HJ, Chen SY, Wang SZ (2004) Extraction and recognition of license plates of motorcycles and vehicles on highways. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004, vol 4, pp 356–359. IEEE.

  4. Comelli P, Ferragina P, Granieri MN, Stabile F (1995) Optical recognition of motor vehicle license plates. IEEE Trans Veh Technol 44(4):790–799

    Article  Google Scholar 

  5. Dharamadhat T, Thanasoontornlerk K, Kanongchaiyos P (2009) Tracking object in video pictures based on background subtraction and image matching. In: Proceedings of the 2008 IEEE international conference on robotics and biomimetics, pp 1255–1260. IEEE.

  6. Cancela B, Ortega M, Penedo MG, Fernández A (2011) Solving multiple-target tracking using adaptive filters. International conference image analysis and recognition. Springer, Berlin, Heidelberg, pp 416–425

    Chapter  Google Scholar 

  7. McIvor A, Zang Q, Klette R (2001) The background subtraction problem for video surveillance systems. International workshop on robot vision. Springer, Berlin, Heidelberg, pp 176–183

    Chapter  Google Scholar 

  8. Sekar G, Deepika M (2015) Complex background subtraction using kalman filter. Int J Eng Res Appl 5(3):15–20

    Google Scholar 

  9. Rabiu H (2013) Vehicle detection and classification for cluttered urban intersection. Int J Comput Sci Eng Appl 3(1):37

    Google Scholar 

  10. Wang K, Liang Y, Xing X, Zhang R (2015) Target detection algorithm based on gaussian mixture background subtraction model. In: Proceedings of the 2015 Chinese intelligent automation conference, pp 439–447. Springer, Berlin, Heidelberg.

  11. Yazdi M, Bagherzadeh MA, Jokar M, Abasi MA (2014) Block-wise background subtraction based on gaussian mixture models. Applied mechanics and materials, vol 490. Trans Tech Publications Ltd, Switzerland, pp 1221–1227

    Google Scholar 

  12. Chan YM, Huang SS, Fu LC, Hsiao PY, Lo MF (2012) Vehicle detection and tracking under various lighting conditions using a particle filter. IET Intell Transp Syst 6(1):1–8

    Article  Google Scholar 

  13. Niknejad HT, Takeuchi A, Mita S, McAllester D (2012) On-road multivehicle tracking using deformable object model and particle filter with improved likelihood estimation. IEEE Trans Intell Transp Syst 13(2):748–758

    Article  Google Scholar 

  14. Long T, Jiao W, He G, Wang W (2013) Automatic line segment registration using Gaussian mixture model and expectation-maximization algorithm. IEEE J Sel Top Appl Earth Obs Remote Sens 7(5):1688–1699

    Article  Google Scholar 

  15. Chen YL, Wu BF, Lin CT, Fan CJ, Hsieh CM (2009) Real-time vision-based vehicle detection and tracking on a moving vehicle for nighttime driver assistance. Int J Robot Autom 24(2):89–102

    Google Scholar 

  16. Sun Z, Bebis G, Miller R (2006) Monocular precrash vehicle detection: features and classifiers. IEEE Trans Image Process 15(7):2019–2034

    Article  Google Scholar 

  17. Junior OL, Nunes U (2008) Improving the generalization properties of neural networks: an application to vehicle detection. In Proceedings of the 2008 11th international IEEE conference on intelligent transportation systems, pp 310–315. IEEE.

  18. Yan G, Yu M, Yu Y, Fan L (2016) Real-time vehicle detection using histograms of oriented gradients and AdaBoost classification. Optik 127(19):7941–7951

    Article  Google Scholar 

  19. Negri P, Clady X, Hanif SM, Prevost L (2008) A cascade of boosted generative and discriminative classifiers for vehicle detection. EURASIP J Adv Signal Process 2008:1–12

    Article  Google Scholar 

  20. Withopf D, Jahne B (2006) Learning algorithm for real-time vehicle tracking. In: Proceedings of the 2006 IEEE intelligent transportation systems conference, pp 516–521. IEEE.

  21. Chen SC, Shyu ML, Peeta S, Zhang C (2005) Spatiotemporal vehicle tracking: the use of unsupervised learning-based segmentation and object tracking. IEEE Robot Autom Mag 12(1):50–58

    Article  Google Scholar 

  22. Uy ACP, Quiros ARF, Bedruz RA, Abad A, Bandala A, Sybingco E, Dadios EP (2016) Automated traffic violation apprehension system using genetic algorithm and artificial neural network. In: Proceedings of the 2016 IEEE region 10 conference (TENCON), pp 2094–2099. IEEE.

  23. City Brain project (2016). https://www.alibabacloud.com/solutions/intelligence-brain/city. Accessed 26 Jan 2020

  24. Vivacity traffic management system (2016). https://vivacitylabs.com/technology/. Accessed 26 Jan 2020

  25. Traffic Congestion Survey (2015). https://www.reuters.com/article/us-usa-traffic-study/u-s-commuters-spend-about-42-hours-a-year-stuck-in-traffic-jams-idUSKCN0QV0A820150826, 2015. Accessed 26 Jan 2020.

  26. Dutta T, Pal G (2010) Pulmonary function test in traffic police personnel in Pondicherry. Indian J Physiol Pharmacol 54(4):329–336

    Google Scholar 

  27. Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767.

  28. Hilario CH, Collado JM, Armingol JM, De La Escalera A (2005) Pyramidal image analysis for vehicle detection. In: Proceedings of the IEEE intelligent vehicles symposium, 2005, pp 88–93. IEEE.

  29. Sun D, Roth S, Black MJ (2010) Secrets of optical flow estimation and their principles. In: Proceedings of the 2010 IEEE computer society conference on computer vision and pattern recognition, pp 2432–2439. IEEE.

  30. Kim G, Kim H, Park J, Yu Y (2011) Vehicle tracking based on kalman filter in tunnel. In: Proceedings of the international conference on information security and assurance, pp 250–256. Springer, Berlin, Heidelberg.

  31. Gustafsson F, Gunnarsson F, Bergman N, Forssell U, Jansson J, Karlsson R, Nordlund PJ (2002) Particle filters for positioning, navigation, and tracking. IEEE Trans Signal Process 50(2):425–437

    Article  Google Scholar 

  32. Exner D, Bruns E, Kurz D, Grundhöfer A, Bimber O (2010) Fast and robust CAMShift tracking. In: Proceedings of the 2010 IEEE computer society conference on computer vision and pattern recognition-workshops, pp 9–16. IEEE.

  33. Bewley A, Ge Z, Ott L, Ramos F, Upcroft B (2016) Simple online and realtime tracking. In: Proceedings of the 2016 IEEE international conference on image processing (ICIP), pp 3464–3468. IEEE.

  34. Open Images Dataset. https://storage.googleapis.com/openimages/web/index.html. Accessed 11 March 2020

  35. COCO Dataset. http://cocodataset.org. Accessed 20 Feb 2020.

  36. Pascal VOC Dataset. http://host.robots.ox.ac.uk/pascal/VOC/. Accessed 14 March 2020.

  37. Stanford Cars Dataset. https://ai.stanford.edu/~jkrause/cars/car_dataset.html. Accessed 26 Feb 2020.

  38. Sivaraman S, Trivedi MM (2010) A general active-learning framework for on-road vehicle recognition and tracking. IEEE Trans Intell Transp Syst 11(2):267–276

    Article  Google Scholar 

  39. Nguyen A, Yosinski J, Clune J (2015) Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 427–436.

  40. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  41. Kulikajevas A, Maskeliunas R, Damasevicius R, Ho ES (2020) 3D object reconstruction from imperfect depth data using extended YOLOv3 network. Sensors 20(7):2025

    Article  Google Scholar 

  42. Li Y, Han Z, Xu H, Liu L, Li X, Zhang K (2019) YOLOv3-lite: a lightweight crack detection network for aircraft structure based on depthwise separable convolutions. Appl Sci 9(18):3781

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Key Research and Development Program of China (Grant No. 2019YFB1405600).

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Authors and Affiliations

  1. State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China

    Mohammed A. A. Al-qaness & Hong Fan

  2. Department of Software Engineering, Foundation University, Islamabad, 44000, Pakistan

    Aaqif Afzaal Abbasi

  3. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt

    Rehab Ali Ibrahim

  4. Software Research Institute, Athlone Institute of Technology, Athlone, Ireland

    Saeed H. Alsamhi

  5. School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230031, Anhui, China

    Ammar Hawbani

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  1. Mohammed A. A. Al-qaness
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  2. Aaqif Afzaal Abbasi
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  3. Hong Fan
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  4. Rehab Ali Ibrahim
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  5. Saeed H. Alsamhi
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  6. Ammar Hawbani
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Correspondence to Mohammed A. A. Al-qaness.

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Al-qaness, M.A.A., Abbasi, A.A., Fan, H. et al. An improved YOLO-based road traffic monitoring system. Computing 103, 211–230 (2021). https://doi.org/10.1007/s00607-020-00869-8

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  • DOI: https://doi.org/10.1007/s00607-020-00869-8

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