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Lightweight Deep Neural Network Approach for Parking Violation Detection

Published: 14 December 2018 Publication History

Abstract

Routine patrolling and inspection of parking violations is a time-consuming and labour-intensive process. As such, a lightweight deep neural network approach is developed to automate parking violation detection in outdoor parking areas. An IP camera is utilized to continuously capture outdoor parking image covering multiple illegal parking regions and feed it to Raspberry Pi. Detection is performed on the Raspberry Pi by fusing the lightweight image classification model with sliding window search program to locate illegally parked vehicles. The system is able to detect double parking violations and vehicles that parked illegally in unmarked area. Multithreading processing is employed to speed up the detection process. An Android-based smartphone application known as the Enforcer App is developed to translate the detection results stored in server into graphical user interface. The application displays live parking violation information at parking areas as well as the position of each illegally parked vehicle to ease parking enforcement. An initial prototype was implemented at an outdoor parking lot of Multimedia University, Malaysia to study its detection performance. Experimental results demonstrate high reliability and robustness of the proposed system with no missed detection and 98.7% precision rate. The parking violation detection in three illegal parking regions are completed within a minimum time of 3.46 seconds.

References

[1]
Shao, W., Salim, F. D., Gu, T., Dinh, N. T. and Chan, J. 2018. Traveling Officer Problem: Managing Car Parking Violations Efficiently Using Sensor Data. IEEE Internet of Things Journal, 5(2), (April. 2018), 802--810.
[2]
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M. and Berg, A.C. 2015. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), (Dec. 2015), 211--252.
[3]
Ng, C. K. and Cheong, S. N. 2018. Cost-Effective Outdoor Car Park System with Convolutional Neural Network on Raspberry Pi. Journal of Engineering and Applied Sciences, 13, (2018), 7062--7067.
[4]
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M. and Adam, H. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (April. 2017).
[5]
Shi, W. and Dustdar, S. 2016. The promise of edge computing. Computer 49(5), (May. 2016), 78--81.
[6]
Sarker, M., Mostafa, K., Weihua, C. and Song, M. K. 2015. 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 13(3), (Sept. 2015), 197--204.
[7]
Jo, K. H. 2017. Cumulative Dual Foreground Differences For Illegally Parked Vehicles Detection. IEEE Transactions on Industrial Informatics 13(5), (Oct. 2017), 2464--2473.
[8]
Akhawaji, R., Sedky, M. and Soliman, A. H. 2017. Illegal parking detection using Gaussian mixture model and kalman filter. In: Computer Systems and Applications (AICCSA), 2017 IEEE/ACS 14th International Conference on, (Oct. 2017), 840--847.
[9]
Kim, A. R., Rhee, S. Y. and Jang, H. W. (2016). Lane Detection for Parking Violation Assessments. International Journal of Fuzzy Logic and Intelligent Systems 16(1), (Mar. 2016), 13--20.
[10]
Xie, X., Wang, C., Chen, S., Shi, G. and Zhao, Z. 2017. Real-Time Illegal Parking Detection System Based on Deep Learning. In: Proceedings of the 2017 International Conference on Deep Learning Technologies, (Jun. 2017), 23--27.
[11]
Ng. C. K., Cheong, S. N., Yap, W. J. and Foo, Y. L. 2018. Outdoor Illegal Parking Detection System Using Convolutional Neural Network on Raspberry Pi. International Journal of Engineering & Technology 7(3.7), (Jul. 2018), 17--20.
[12]
A. Krizhevsky, I. Sutskever and G. E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, (2012), 1097--1105.
[13]
Zoph, B., Vasudevan, V., Shlens, J. and Le, Q. V. 2017. Learning transferable architectures for scalable image recognition. arXiv preprint arXiv:1707.07012, 2(6), (Jul. 2017).
[14]
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. 2014. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15(1), (Jan. 2014), 1929--1958.

Cited By

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  • (2024)A Data-Driven Crowdsensing Framework for Parking Violation DetectionIEEE Transactions on Mobile Computing10.1109/TMC.2023.333142923:6(6921-6935)Online publication date: Jun-2024
  • (2020)CVRRSS‐CHD: Computer vision‐related roadside surveillance system using compound hierarchical‐deep modelsIET Intelligent Transport Systems10.1049/iet-its.2019.083414:11(1353-1362)Online publication date: 15-Sep-2020
  • (2019)Seatbelt Recognition Method Based on Convolutional Attention Mechanism2019 IEEE 2nd International Conference on Computer and Communication Engineering Technology (CCET)10.1109/CCET48361.2019.8989308(187-192)Online publication date: Aug-2019

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    ICNCC '18: Proceedings of the 2018 VII International Conference on Network, Communication and Computing
    December 2018
    372 pages
    ISBN:9781450365536
    DOI:10.1145/3301326
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 14 December 2018

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    Author Tags

    1. Convolutional Neural Network
    2. Deep Learning
    3. Image Classification
    4. Multithreading
    5. Parking Violation Detection

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    View all
    • (2024)A Data-Driven Crowdsensing Framework for Parking Violation DetectionIEEE Transactions on Mobile Computing10.1109/TMC.2023.333142923:6(6921-6935)Online publication date: Jun-2024
    • (2020)CVRRSS‐CHD: Computer vision‐related roadside surveillance system using compound hierarchical‐deep modelsIET Intelligent Transport Systems10.1049/iet-its.2019.083414:11(1353-1362)Online publication date: 15-Sep-2020
    • (2019)Seatbelt Recognition Method Based on Convolutional Attention Mechanism2019 IEEE 2nd International Conference on Computer and Communication Engineering Technology (CCET)10.1109/CCET48361.2019.8989308(187-192)Online publication date: Aug-2019

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