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Pixel matching search algorithm for counting moving vehicle in highway traffic videos

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Abstract

Traffic monitoring through video processing is one of the hot research areas in the Intelligent Transportation System (ITS). Vehicle counting systems should be simple enough to be applied in real-time circumstances. A novel and fast algorithm for vehicle counting from a traffic video sequence is proposed in this paper where the vehicle tracking step is not necessary. A reference model is only created in the video frames for a narrow area. When going through this narrow area, the moving vehicles are identified as foreground objects. Detection of moving vehicles is achieved by integrating approximated median filter based background subtraction with binary integral projection. The detected candidates are counted as a vehicle using a novel pixel matching search algorithm. The proposed algorithm does not rely on every video frame. It only requires every third frame for processing and thus increases the computation speed by three times compared to existing techniques. The proposed algorithm is tested and validated on a standard data set as well as a custom data set. Two parameters such as accuracy and processing time are used for the system evaluation where an overall accuracy of 96.84% is achieved. The processing time results show that the proposed system can perform in real-time with an average real-time processing speed of 93.92%.

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References

  1. Abdelwahab M (2019) Accurate vehicle counting approach based on deep neural networks. 1–5 https://doi.org/10.1109/ITCE.2019.8646549

  2. Alessandretti G, Broggi A, Cerri P (2007) Vehicle and guard rail detection using radar and vision data fusion. IEEE Trans Intell Transp Syst 8(1):95–105. https://doi.org/10.1109/TITS.2006.888597

    Article  Google Scholar 

  3. Barnich O, Van Droogenbroeck M (2011) Vibe: A universal background subtraction algorithm for video sequences. IEEE Trans Image Process 20(6):1709–1724. https://doi.org/10.1109/TIP.2010.2101613

    Article  MathSciNet  Google Scholar 

  4. Bas E, Tekalp AM, Salman FS (2007) Automatic vehicle counting from video for traffic flow analysis. In: 2007 IEEE intelligent vehicles symposium, pp 392–397

  5. Bouaich S, Mahraz MA, Riff J, Tairi H (2018) Vehicle counting system in real-time. In: 2018 International conference on intelligent systems and computer vision (ISCV), pp 1–4. https://doi.org/10.1109/ISACV.2018.8354033

  6. Bouvié C, Scharcanski J, Barcellos P, Escouto FL (2013) Tracking and counting vehicles in traffic video sequences using particle filtering. In: 2013 IEEE international instrumentation and measurement technology conference (I2MTC), pp 812–815 https://doi.org/10.1109/I2MTC.2013.6555527

  7. Chen L, Zhang Z, Peng L (2018) Fast single shot multibox detector and its application on vehicle counting system. IET Intell Transp Sy 12 (10):1406–1413. https://doi.org/10.1049/iet-its.2018.5005

    Article  Google Scholar 

  8. Dai Z, Song H, Wang X, Fang Y, Yun X, Zhang Z, Li H (2019) Video-based vehicle counting framework. IEEE Access 7:64460–64470. https://doi.org/10.1109/ACCESS.2019.2914254

    Article  Google Scholar 

  9. Engel JI, Martín J, Barco R (2017) A low-complexity vision-based system for real-time traffic monitoring. IEEE Trans Intell Transp Syst 18(5):1279–1288

    Article  Google Scholar 

  10. García-Mateos G, Ruiz A, López-de Teruel P (2002) Face detection using integral projection models. 2396:644–653. https://doi.org/10.1007/3-540-70659-3_67

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

  12. Guerrero-Gómez-Olmedo R, López-Sastre RJ, Maldonado-Bascón S, Fernández-Caballero A, De la Paz lópez F, Toledo Moreo FJ (2013) Vehicle tracking by simultaneous detection and viewpoint estimation. In: Ferrández vicente JM, Álvarez Sánchez JR (eds) Natural and artificial computation in engineering and medical applications. Springer, Berlin, pp 306–316

  13. Guo J, He H, He T, Lausen L, Li M, Lin H, Shi X, Wang C, Xie J, Zha S, Zhang A, Zhang H, Zhang Z, Zhang Z, Zheng S, Zhu Y (2020) Gluoncv and gluonnlp: Deep learning in computer vision and natural language processing. J Mach Learn Res 21(23):1–7. http://jmlr.org/papers/v21/19-429.html

    MATH  Google Scholar 

  14. Jo Y, Jung I (2014) Analysis of vehicle detection with wsn-based ultrasonic sensors. Sensors 14:4050–14069. https://doi.org/10.3390/s140814050

    Article  Google Scholar 

  15. Kamkar S, Safabakhsh R (2016) Vehicle detection, counting and classification in various conditions. IET Intell Transp Syst 10(6):406–413

    Article  Google Scholar 

  16. Li DL, Prasad M, Liu C, Lin C (2020) Multi-view vehicle detection based on fusion part model with active learning. IEEE Trans Intell Transp Syst, 1–12

  17. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. In: European conference on computer vision. Springer, pp 21–37

  18. Liu F, Zeng Z, Jiang R (2017) A video-based real-time adaptive vehicle-counting system for urban roads. PLOS ONE 12 (11):1–16. https://doi.org/10.1371/journal.pone.0186098

    Google Scholar 

  19. Mimbela LEY, Klein LA (2000) Summary of vehicle detection and surveillance technologies used in intelligent transportation systems. Tech. rep. Federal Highway Administration s (FHWA) Intelligent Transportation Systems Joint Program Office

  20. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cyber 9(1):62–66. https://doi.org/10.1109/TSMC.1979.4310076

    Article  Google Scholar 

  21. Park MW, In Kim J, Lee YJ, Park J, Suh W (2017) Vision-based surveillance system for monitoring traffic conditions. Multimed Tools Appl 76(23):25343–25367. https://doi.org/10.1007/s11042-017-4521-4

    Article  Google Scholar 

  22. Perttunen M, Kostakos V, Riekki J, Ojala T (2015) Urban traffic analysis through multi-modal sensing. Pers Ubiquit Comput 19(3):709–721. https://doi.org/10.1007/s00779-015-0833-4

    Article  Google Scholar 

  23. Quesada J, Rodriguez P (2016) Automatic vehicle counting method based on principal component pursuit background modeling. In: 2016 IEEE International conference on image processing (ICIP), pp 3822–3826 https://doi.org/10.1109/ICIP.2016.7533075

  24. Rabbouch H, Saâdaoui F, Mraihi R (2017) Unsupervised video summarization using cluster analysis for automatic vehicles counting and recognizing. Neurocomputing 260:157–173. http://www.sciencedirect.com/science/article/pii/S0925231217306963

    Article  Google Scholar 

  25. Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv:180402767

  26. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems. pp 91–99

  27. Seenouvong N, Watchareeruetai U, Nuthong C, Khongsomboon K, Ohnishi N (2016) A computer vision based vehicle detection and counting system. In: 2016 8th International conference on knowledge and smart technology (KST), pp 224–227. https://doi.org/10.1109/KST.2016.7440510

  28. Shen J, Tao D, Li X (2008) Modality mixture projections for semantic video event detection. IEEE Trans Circ Syst Vid Tech 18(11):1587–1596

    Article  Google Scholar 

  29. Sobral A, Vacavant A (2014) A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Comput Vis Image Und 122:4–21. https://doi.org/10.1016/j.cviu.2013.12.005

    Article  Google Scholar 

  30. Tao H, Lu X (2018) Smoky vehicle detection based on multi-feature fusion and ensemble neural networks. Multimed Tools Appl 77(24):32153–32177. https://doi.org/10.1007/s11042-018-6248-2

    Article  Google Scholar 

  31. Tourani A, Shahbahrami A (2015) Vehicle counting method based on digital image processing algorithms. In: 2015 2nd International conference on pattern recognition and image analysis (IPRIA), pp 1–6. https://doi.org/10.1109/PRIA.2015.7161621

  32. Wang H, Cai Y, Chen X, Chen L (2015) Occluded vehicle detection with local connected deep model. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-015-3141-0

  33. Wang P, Lai J, Huang Z, Tan Q, Lin T (2020) Estimating traffic flow in large road networks based on multi-source traffic data. IEEE Trans Intell Transp Syst, 1–12

  34. Wang G, Xiao D, Gu J (2008) Review on vehicle detection based on video for traffic surveillance. In: 2008 IEEE international conference on automation and logistics, pp 2961–2966 https://doi.org/10.1109/ICAL.2008.4636684

  35. Wicaksono DW, Setiyono B (2017) Speed estimation on moving vehicle based on digital image processing. Int J Comp Sci Appl Math 3:21–26

    Article  Google Scholar 

  36. Wu C, Lin C, Lee C (2012) Applying a functional neurofuzzy network to real-time lane detection and front-vehicle distance measurement. IEEE Trans Syst Man Cy Part C Appl Rev 42(4):577–589

    Article  Google Scholar 

  37. Xia Y, Shi X, Song G, Geng Q, Liu Y (2016) Towards improving quality of video-based vehicle counting method for traffic flow estimation. Signal Process 120:672–681. http://www.sciencedirect.com/science/article/pii/S016516841400499X

    Article  Google Scholar 

  38. Xu H, Zhou W, Zhu J, Huang X, Wang W (2017) Vehicle counting based on double virtual lines. SIViP 11(5):905–912

    Article  Google Scholar 

  39. Yang H (2018) Real-time vehicle detection and counting in complex traffic scenes using background subtraction model with low-rank decomposition. IET Intell Trans Syst 12(10):75–85. https://digital-library.theiet.org/content/journals/10.1049/iet-its.2017.0047

    Article  Google Scholar 

  40. Zhang Y, Zhao C, Zhang Q (2017) Counting vehicles in urban traffic scenes using foreground time-spatial images. IET Intell Transp Sy 11(2):61–67. https://doi.org/10.1049/iet-its.2016.0162

    Article  Google Scholar 

  41. Zhao K, Deng J, Cheng D (2018) Real-time moving pedestrian detection using contour features. Multimed Tools Appl 77(23):30891–30910. https://doi.org/10.1007/s11042-018-6173-4

    Article  Google Scholar 

  42. Zhao ZQ, Zheng P, Xu ST, Wu X (2019) Object detection with deep learning: a review. IEEE Transactions on Neural Networks and Learning Systems PP:1–21, https://doi.org/10.1109/TNNLS.2018.2876865

  43. Zivkovic Z (2004) Improved adaptive gaussian mixture model for background subtraction. In: Proceedings of the 17th international conference on pattern recognition. ICPR 2004, vol 2, pp 28–31

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Acknowledgements

This work was funded by Vandi Technologies PTE LTD Singapore, (Grant No. VANDI/PS01/NITT1821 dated 10-09-2018)

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  1. National Institutte of Technology Tiruchirapalli, Tamilnadu, India

    Harikrishnan P. M., Anju Thomas, Nisha J. S., Varun P. Gopi & P. Palanisamy

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  1. Harikrishnan P. M.
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  2. Anju Thomas
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Correspondence to Varun P. Gopi.

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P. M., H., Thomas, A., J. S., N. et al. Pixel matching search algorithm for counting moving vehicle in highway traffic videos. Multimed Tools Appl 80, 3153–3172 (2021). https://doi.org/10.1007/s11042-020-09666-z

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