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A traffic load analysis model using optical flow and boosting classifier

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Abstract

High traffic load is an unfortunate and avoidable companion, especially in urban cities where an enormous number of vehicles move every day from one area to other. However, this situation can be controlled and many problems that occur due to heavy traffic congestion can be avoided if available traffic data is intelligently analyzed, planned, maintained and classified. Therefore, Traffic Load Analyser (TLA) model is presented under the Intelligent Traffic Management System (ITMS) scheme to control high traffic congestion and analyse traffic flow through vehicle tracking. Here, local and global optical flow methods are used to evaluate traffic flow as light traffic and heavy traffic respectively. Here, multi-variant features are determined using optical flow analysis methods to handle traffic congestion problems efficiently. Further, obtained object boundaries help to track vehicles in traffic congestion. Here, Performance results are achieved using the UCSD dataset. Here, performance results are carried out under various weather conditions like rainy weather and overcast weather for all three classes such as light, moderate and heavy traffic. High classification accuracy is achieved using the proposed TLA method as 99.98% and compared against several state-of-art-techniques.

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Data availability

The datasets used during and/or analyzed during the current study are available in reference [11, 26].

References

  1. Andrews Sobral LO, Schnitman L, De Souza F (2013) "Highway traffic congestion classification using holistic properties," in 10th IASTED Int Conf Signal Process Pattern Recognit pp. 458–465

  2. Asmaa O, Mokhtar K, Abdelaziz O (2013) Road traffic density estimation using microscopic and macroscopic parameters. Image Vis Comput 31(11):887–894

    Article  Google Scholar 

  3. Bernas M et al (2018) A survey and comparison of low-cost sensing technologies for road traffic monitoring. Sensors 1810):3243

  4. Bland L-P, Brent DA (2018) Traffic and crime. J Public Econ 160:96–116

    Article  Google Scholar 

  5. Buch N, Velastin SA, Orwell J (2011) A review of computer vision techniques for the analysis of urban traffic. IEEE Trans Intell Transp Syst 12(3):920–939

  6. Cao Z et al (2020) Haze removal of railway monitoring images using multi-scale residual network. IEEE Trans Intell Transport Syst 22(12):7460–7473

  7. Chakraborty P, Adu-Gyamfi YO, Poddar S, Ahsani V, Sharma A, Sarkar S (2018) Traffic congestion detection from camera images using deep convolution neural networks. Transp Res Rec, J Transp Res Board 2672(45):222–231

    Article  Google Scholar 

  8. Chandrasekhar M, Sai Krishna C, Chakra Dhār B, Phaneendra P, Sasanka C (2013) Traffic control using digital image processing. Int J Adv Electric Electron Eng 2(5):96–100

  9. Chen K, Ota M, Dong CY, Jin H (2020) WITM: intelligent traffic monitoring using fine-grained wireless signal. IEEE Trans Emerg Top Comput Intell 4(3):206–215. https://doi.org/10.1109/TETCI.2019.2926505

    Article  Google Scholar 

  10. Datondji RE, 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 

  11. FHWA (n.d.) The 2016 Traffic Detector Handbook. Accessed: Apr. 12, 2019. [Online]. Available: https://www.fhwa.dot.gov/publications/research/operations/its/06108/06108.pdf

  12. Inigo RM (1985) Traffic monitoring and control using machine vision: a survey. IEEE Trans Ind Electron IE-32(3):177–185

    Article  Google Scholar 

  13. Jain NK, Saini R, Mittal P (2019) A review on traffic monitoring system techniques. In: Soft computing: theories and applications. Springer, Singapore, pp 569–577

    Chapter  Google Scholar 

  14. Loce R, Bala R, Trivedi M (eds) (2017) Computer vision and imaging in intelligent transportation systems. John Wiley & Sons, New York

  15. Luo Z, Jodoin P-M, Li S-Z, Su S-Z (2015) “Traf_c analysis without motion features,” in Proc. IEEE Int. Conf. Image Process. (ICIP), pp. 3290_3294

  16. Luo Z, Jodoin P-M, Su S-Z, Li S-Z, Larochelle H (2018) Traf_c analytics with low-frame-rate videos. IEEE Trans Circuits Syst Vid Technol 28(4):878–891

    Article  Google Scholar 

  17. Luo Z, Jodoin P-M, Su S-Z, Li S-Z, Larochelle H (2018) Traffic analytics with low-frame-rate videos. IEEE Trans Circuits Syst Video Technol 28(4):878–891

    Article  Google Scholar 

  18. Pamula T (2018) Road traffic conditions classification based on multilevel filtering of image content using convolutional neural networks. IEEE Intell Transp Syst Mag 10(3):11–21

    Article  Google Scholar 

  19. Puri A (2005) A survey of unmanned aerial vehicles (UAV) for traffic surveillance. Department of computer science and engineering, University of South Florida, pp 1–29

  20. Riaz, Khan SA (2013) “Traf_c congestion classi_cation using motion vector statistical features,” in Proc 6th Int Conf Mach Vis (ICMV), vol. 9067, Art no 90671A

  21. Ribeiro MVL, Samatelo JLA, Bazzan ALC (n.d.) "A New Microscopic Approach to Traffic Flow Classification Using a Convolutional Neural Network Object Detector and a Multi-Tracker Algorithm," in IEEE Trans Intell Transport Syst, https://doi.org/10.1109/TITS.2020.3040594.

  22. Tian B, Yao Q, Gu Y, Wang K, Li Y (2011) Video processing techniques for traffic flow monitoring: A survey. In: 2011 14th international IEEE conference on intelligent transportation systems (ITSC). IEEE, pp 1103–1108

  23. Tyburski R (1988) A review of road sensor technology for monitoring vehicle traffic. Inst Transp Eng J 59(8):27–29

    Google Scholar 

  24. Wald K, Shetty J (2014) Traffic Light Control System Using Image Processing. Int J Innov Res Comput Commun Eng 2(5):289

    Google Scholar 

  25. Won M (2020) Intelligent traffic monitoring Systems for Vehicle Classification: a survey. IEEE Access 8:73340–73358. https://doi.org/10.1109/ACCESS.2020.2987634

    Article  Google Scholar 

  26. Won M, Sahu S, Park K.-J (2018) “DeepWiTraffic: Low-cost WiFi-based traffic monitoring system using deep learning,” arXiv:1812.08208. [Online]. Available: http://arxiv.org/abs/1812.08208

  27. Zhang L, Zhu J, Ni CH, Shen X (2020) Verifiable and privacy-preserving traffic flow statistics for advanced traffic management systems. IEEE Trans Veh Technol 69(9):10336–10347. https://doi.org/10.1109/TVT.2020.3005363

    Article  Google Scholar 

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  1. Faculty of Engineering & Technology (Exclusively for Women), Sharnbasva University, Kalaburagi, Karnataka, India

    Pushpalata & M. Sasikala

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Pushpalata, Sasikala, M. A traffic load analysis model using optical flow and boosting classifier. Multimed Tools Appl 82, 40783–40798 (2023). https://doi.org/10.1007/s11042-023-14878-0

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