Parking Occupancy Detection: A Lightweight Deep Neural Network Approach

  • Chin-Kit NgEmail author
  • Soon-Nyean Cheong
  • Yee-Loo Foo
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 536)


Inaccessibility of real-time parking occupancy information may cause inefficiency in parking management. This paper proposed a novel lightweight deep neural network approach to realize outdoor parking occupancy detection system to support more efficient parking management. A lightweight MobileNet binary classifier is used to accurately identify the occupancy status of parking space image patches that are extracted from live parking lot camera feeds. A performance comparison between different network configurations of MobileNet has been done to investigate their speed-accuracy trade-off when running on embedded device. The prototype was deployed at an outdoor campus parking to evaluate effectiveness of the proposed system. The prototype can detect 22 parking spaces within 2.4 s when running on an ASUS Tinker Board and achieve a detection accuracy of 99%.


Lightweight deep neural network Parking occupancy detection 



The authors thankfully acknowledge the financial supports provided by the Telekom Malaysia Research and Development Grant (No. RDTC170946) to successfully implement the prototype at Multimedia University (Cyberjaya, Malaysia).


  1. 1.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Adam, H.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
  3. 3.
    Liu, J., Mohandes, M., Deriche, M.: A multi-classifier image based vacant parking detection system. In: 2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS), pp. 933–936, Abu Dhabi (2013)Google Scholar
  4. 4.
    Al-Kharusi, H., Al-Bahadly, I.: Intelligent parking management system based on image processing. World J. Eng. Technol. 2(2), 55–67 (2014)CrossRefGoogle Scholar
  5. 5.
    Nieto, R.M., García-Martín, Á., Hauptmann, A.G., Martínez, J.M.: Automatic vacant parking places management system using multicamera vehicle detection. IEEE Trans. Intell. Transp. Syst. 20, 1069–1080 (2018)CrossRefGoogle Scholar
  6. 6.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real time object detection with region proposal networks. In: Proceedings CVPR, pp. 91–99 (2015)Google Scholar
  7. 7.
    Amato, G., Carrara, F., Falchi, F., Gennaro, C., Vairo, C.: Car parking occupancy detection using smart camera networks and deep learning. In: 2016 IEEE Symposium on Computers and Communication (ISCC). IEEE (2016)Google Scholar
  8. 8.
    de Almeida, P.R., Oliveira, L.S., Britto, A.S., Silva, E.J., Koerich, A.L.: PKLot–a robust dataset for parking lot classification. Expert Syst. Appl. 42(11), 4937–4949 (2015)CrossRefGoogle Scholar
  9. 9.
    Martinez, J., Zoeke, D., Vossiek, M.: A convolutional neural network approach to parking monitoring in urban radar sensing. In: 2017 European Radar Conference (EURAD), pp. 86–89 (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Faculty of EngineeringMultimedia UniversityCyberjayaMalaysia

Personalised recommendations