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Template Mask based Parking Car Slots Detection in Aerial Images

  • Wirabudi, Andri Agustav (Department of Intelligence Media Engineering, Hanbat National University) ;
  • Han, Heeji (Department of Multimedia Engineering, Hanbat National University) ;
  • Bang, Junho (Department of Information and Communication Engineering, Hanbat National University) ;
  • Choi, Haechul (Department of Intelligence Media Engineering, Hanbat National University)
  • Received : 2022.10.13
  • Accepted : 2022.12.13
  • Published : 2022.12.20

Abstract

The increase in vehicle purchases worldwide is having a very significant impact on the availability of parking spaces. In particular, since it is difficult to secure a parking space in an urban area, it may be of great help to the driver to check vehicle parking information in advance. However, the current parking lot information is still operated semi-manually, such as notifications. Therefore, in this study, we propose a system for detecting a parking space using a relatively simple image processing method based on an image taken from the sky and evaluate its performance. The proposed method first converts the captured RGB image into a black-and-white binary image. This is to simplify the calculation for detection using discrete information. Next, a morphological operation is applied to increase the clarity of the binary image, and a template mask in the form of a bounding box indicating a parking space is applied to check the parking state. Twelve image samples and 2181 total of test, were used for the experiment, and a threshold of 40% was used to detect each parking space. The experimental results showed that information on the availability of parking spaces for parking users was provided with an accuracy of 95%. Although the number of experimental images is somewhat insufficient to address the generality of accuracy, it is possible to confirm the possibility of parking space detection with a simple image processing method.

Keywords

Acknowledgement

This research was support by the National Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1F1A1070302).

References

  1. M. N. Mubin, H. Kusuma, and M. Rivai, "Identification of Parking Lot Status Using Circle Blob Detection," Proceedings - 2021 International Seminar on Intelligent Technology and Its Application: Intelligent Systems for the New Normal Era, ISITIA 2021, pp. 261-265, 2021. doi: https://doi.org/10.1109/ISITIA52817.2021.9502191
  2. D. Acharya, W. Yan, and K. Khoshelham, "Real-time image-based parking occupancy detection using deep learning," CEUR Workshop Proc, vol. 2087, pp. 33-40, 2018, Retrieved from https://www.researchgate.net/publication/323796590_Real-time_image-based_parking_occupancy_detection_using_deep_learning
  3. 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 Transactions on Circuits and Systems for Video Technology, vol. 19, no. 7, pp. 1014-1024, 2009. doi: https://doi.org/10.1109/TCSVT.2009.2020249
  4. P. Tatulea, F. Calin, R. Brad, L. Brancovean, and M. Greavu, "An image feature-based method for parking lot occupancy," Future Internet, vol. 11, no. 8, Aug. 2019. doi: https://doi.org/10.3390/fi11080169
  5. K. Pannerselvam, "Adaptive Parking Slot Occupancy Detection Using Vision Transformer and LLIE," 2021 IEEE International Smart Cities Conference, ISC2 2021, 2021. doi: https://doi.org/10.1109/ISC253183.2021.9562955
  6. J. -Y. Chen and C. -M. Hsu, "A visual method tor the detection of available parking slots," 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada, 2017, pp. 2980-2985. doi: https://doi.org/10.1109/SMC.2017.8123081
  7. M. Noor and A. Shrivastava, "Automatic Parking Slot Occupancy Detection using Laplacian Operator and Morphological Kernel Dilation," Proceedings - 2021 IEEE 10th International Conference on Communication Systems and Network Technologies, CSNT 2021, pp. 825-831, 2021. doi: https://doi.org/10.1109/CSNT51715.2021.9509620
  8. A. Kadir and A. Susanto, "Morfologi untuk Pengolahan Citra," Teori dan Aplikasi Pengolahan Citra, pp. 171-229, 2012. ISBN: 978-979-29-3430-4
  9. A. Coleiro, D. Scerri, and I. Briffa, "Car parking detection in a typical village core street using public camera feeds," IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin, vol. 2020-Novem, 2020. doi: https://doi.org/10.1109/ICCE-Berlin50680.2020.9352169
  10. J. K. Suhr and H. G. Jung, "Sensor fusion-based vacant parking slot detection and tracking," IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 1, pp. 21-36, 2014. doi: https://doi.org/10.1109/TITS.2013.2272100
  11. S. Jiang, H. Jiang, S. Ma, and Z. Jiang, "Detection of parking slots based on mask R-CNN," Applied Sciences (Switzerland), vol. 10, no. 12, 2020. doi: https://doi.org/10.3390/app10124295
  12. E. Lu, W. Xie, and A. Zisserman, "Class-Agnostic Counting" Asian Conference on Computer Vision (ACCV) 2018. doi: https://doi.org/10.48550/arXiv.1811.00472