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Automating Paid Parking System Using IoT Technology

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1528))

Abstract

The system aims to propose an efficient pay and park method developed based on use of domains like deep learning, server and app management. The system so developed detects entry of vehicle in parking facility. The database is created and modified with each incoming car and registrations in the system. The user can register his all the vehicles in the system database through an android based PayPark app. The app incorporates Google Pay services to provide the user with a safe and secure payment facility. At the entry and exit of a parking facility, the vehicle is detected along with the time for which it was parked. The corresponding entries are made in the database and saved. Through the PayPark app the charges are deducted from a pre- registered app user. The charges deduction is depending on the time for which the vehicle uses parking facility.

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References

  1. Ozbay, S., Ercelebi, E.: Automatic vehicle identification by plate recognition. World. Acad. Sci. Eng. Technol. Int. J. Comput. Inf. Eng. 1, 1–9 (2007)

    Google Scholar 

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

    Article  Google Scholar 

  3. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  4. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision – ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I, pp. 818–833. Springer International Publishing, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53

    Chapter  Google Scholar 

  5. Yusnita, R., Norbaya, F., Bashruddin, N.: Intelligent parking space detection system based on image processing. Int. J. Innov. Manage. Technol. 3, 232–235 (2012)

    Google Scholar 

  6. Maggo, S., Aswani, R.: AutoPark: a sensor based automated, secure and secure efficient parking guidance system. India IOSR J. Comput. Eng. (IOSRJCE) 8(3), 47–56 (2013)

    Article  Google Scholar 

  7. Android app development documentation. https://developer.android.com/reference/packag

  8. Wang, J., Zhou, W., Xue, J., Liu, X.: The research and realization of vehicle license plate character segmentation and recognition technology. In: 2010 International Conference on Wavelet Analysis and Pattern Recognition, Qingdao, 11–14 July 2010

    Google Scholar 

  9. Smith, R.: An overview of the Tesseract OCR engine in ninth international conference on document analysis and recognition (ICDAR 2007)

    Google Scholar 

  10. Li, Q., An, W., Zhou, A., Ma, L.: Recognition of offline handwritten Chinese characters using the Tesseract open source OCR engine. In: 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (2016)

    Google Scholar 

  11. Yang, J., Hu, B., Yu, J., An, J., Xiong, G.: A License Plate Recognition System Based on Machine Vision. In: IEEE 978–1–4799–0530–0/13

    Google Scholar 

  12. Smart Parking Assist System using Internet of Things (IoT), Int. J. Control Theory App. 9, 40 (2016)

    Google Scholar 

  13. Basavaraju, S.R.: Automatic smart parking system using Internet of Things (IoT). Int. J. Sci. Res. Publ. 5(12), 1–10 (2015)

    Google Scholar 

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Desai, A., Deotale, A., Bapat, A., Khinvasara, C. (2022). Automating Paid Parking System Using IoT Technology. In: Garg, D., Jagannathan, S., Gupta, A., Garg, L., Gupta, S. (eds) Advanced Computing. IACC 2021. Communications in Computer and Information Science, vol 1528. Springer, Cham. https://doi.org/10.1007/978-3-030-95502-1_31

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  • DOI: https://doi.org/10.1007/978-3-030-95502-1_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95501-4

  • Online ISBN: 978-3-030-95502-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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