Outdoor Illegal Parking Detection System Using Convolutional Neural Network on Raspberry Pi

  • Authors

    • Chin Kit Ng
    • Soon Nyean Cheong
    • Wen Wen-Jiun Yap
    • Yee Loo Foo
    2018-07-04
    https://doi.org/10.14419/ijet.v7i3.7.16197
  • Convolutional Neural Network, Illegal Parking Detection, Raspberry Pi

  • This paper proposes a cost-effective vision-based outdoor illegal parking detection system, iConvPark, to automatize the detection of illegally parked vehicles by providing real-time notification regarding the occurrences and locations of illegal parking cases, thereby improving effectiveness of parking rules and regulations enforcement. The iConvPark is implemented on a Raspberry Pi with the use of Convolutional Neural Network as the classifier to identify illegally parked vehicles based on live parking lot image retrieved via an IP camera. The system has been implemented at a university parking lot to detect illegal parking events. Evaluation results show that our proposed system is capable of detecting illegally parked vehicles with precision rate of 1.00 and recall rate of 0.94, implying that the detection is robust against changes in light intensity and the presence of shadow effects under different weather conditions, attributed to the superiority offered by CNN.

     

     

  • References

    1. [1] Cullinane, Kevin, and Polak, J., Illegal Parking And The Enforcement Of Parking Regulations: Causes, Effects, And Interactions, Transport Reviews, 1992. 12(1): p. 49-75.

      [2] J.T. Lee, M.S. Ryoo, M. Riley, and J. K. Aggarwal, Real-Time Illegal Parking Detection in Outdoor Environments Using 1-D Transformation, IEEE Transaction on Circuits and Systems for Video Technology, 2009. 19(7): p. 1014-1024.

      [3] Waqas Hassan, Philip Birch, Rupert Young, and Chris Chatwin, Real-Time Occlusion Tolerant Detection of Illegally Parked Vehicles, International Journal of Control, Automation and Systems, 2012. 10(5): p. 972-981.

      [4] Sarker, Md. Mostafa Kamal, Cai Weihua, and Moon Kyou Song, Detection and Recognition of Illegally Parked Vehicles Based on an Adaptive Gaussian Mixture Model and a Seed Fill Algorithm, Journal of information and communication convergence engineering, 2015. 13(3): p. 197-204.

      [5] Bulan, O., Loce, R. P., Wu, W., Wang, Y., Bernal, E. A., and Fan, Z., Video-based real-time on-street parking occupancy detection system, Journal of Electronic Imaging, 2013. 22(4): p. 041109.

      [6] Liao, R., Roman, C., Ball, P., Ou, S., and Chen, L., Crowdsourcing On-street Parking Space Detection, arXiv preprint, 2016. arXiv:1603.00441.

      [7] Xie, H., Wu, Q., Chen, B., Chen, Y., and Hong, S., Vehicle Detection in Open Parks Using a Convolutional Neural Network, Intelligent Systems Design and Engineering Applications (ISDEA), 2015 Sixth International Conference on, IEEE, 2015. p. 927-930.

      [8] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton, Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, 2012. p. 1097–1105.

      [9] Matthew D Zeiler, Rob Fergus, Visualizing and Understanding Convolutional Networks, ECCV 2014, Part I, LNCS 8689, 2014. p. 818-833.

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  • How to Cite

    Kit Ng, C., Nyean Cheong, S., Wen-Jiun Yap, W., & Loo Foo, Y. (2018). Outdoor Illegal Parking Detection System Using Convolutional Neural Network on Raspberry Pi. International Journal of Engineering & Technology, 7(3.7), 17-20. https://doi.org/10.14419/ijet.v7i3.7.16197