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Faster R-CNN based Automatic Parking Space Detection

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Published:17 December 2020Publication History

ABSTRACT

In this paper, we present a Faster R-CNN based object detection scheme to automatically map the parking spaces in a parking lot, instead of manually mapping them. The work addresses an important gap in the recent computer vision based artificial intelligence techniques to build smart parking systems. Our results show that our approach decreases the human effort needed by upto a compelling 86%. We show that the percentage of the available parking spots that are automatically detected through our approach accumulates over time and, in theory, can approach a 100%, on a day when all the parking spots are fully occupied. In other words, the approach is designed to have its highest performance over a busy parking lot during the busiest time.

References

  1. G. Bill Yang Cai, Ricardo Alvarez, Michelle Sit, Fábio Duarte, Carlo Ratti, “Deep Learning-Based Video System for Accurate and Real-Time Parking Measurement,” IEEE Internet of Things Journal , vol. 6, no. 5, pp. 7693-7701, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  2. “World Urbanization Prospects,” United Nations Organization, 2014. [Online]. Available: https://www.un.org/en/development/desa/publications/2014-revision-world-urbanizationprospects.html. [Accessed 07-15-2020].Google ScholarGoogle Scholar
  3. “Automated Vehicles for Safety,” National Highway Traffic Safety Administration, Available: https://www.nhtsa.gov/technology-innovation/automated-vehicles-safety. [Accessed 07-15-2020].Google ScholarGoogle Scholar
  4. T. Lin, H. Rivano and F. Le Mouël, "A Survey of Smart Parking Solutions," in IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 12, pp. 3229-3253, December. 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. T. Eric and M. Praveen, “SParkSys: A Framework for Smart Parking Systems,” International Conference on Computational Science and Computational Intelligence, 2017.Google ScholarGoogle Scholar
  6. G. Amato, F. Carrara, F. Falchi, C. Gennaro and C. Vairo, “Car parking occupancy detection using smart camera networks and Deep Learning,” IEEE Symposium on Computers and Communication, 2016, pp. 1212-1217.Google ScholarGoogle ScholarCross RefCross Ref
  7. S. Valipour, M. Siam, E. Stroulia, M. Jagersand, “Parking-stall vacancy indicator system, based on deep convolutional neural networks,” in IEEE 3rd World Forum on Internet of Things, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  8. S. Ren, K. He, R. Girshick and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Y. Wu, A. Kirillov, F. Massa, W. Lo and R. Girshick, “Detectron2,” 2019. [Online]. Available: https://github.com/facebookresearch/detectron2. [Accessed 07-15-2020].Google ScholarGoogle Scholar
  10. T. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan and S. Belongie, “Feature Pyramid Networks for Object Detection,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 936-944.Google ScholarGoogle ScholarCross RefCross Ref
  11. H. Meng-Ru, L. Yen-Liang and H. Winston, “Drone-Based Object Counting by Spatially Regularized Regional Proposal Network,” IEEE International Conference on Computer Vision (ICCV) 4165-4173, 2017.Google ScholarGoogle Scholar
  12. . Lin, M. Maire, S. Belongie, H. James, P. Perona, R. Deva, D. Piotr, C. L. Zitnick, “Microsoft COCO: Common Objects in Context,” European Conference on Computer Vision, 2014 pp 740-755.Google ScholarGoogle Scholar
  13. G. Amato, F. Carrara, F. Falchi, C. Gennaro, C. Vairo, “CNRPark+EXT A Dataset for Visual Occupancy Detection of Parking Lots,” [Online.] Available: http://cnrpark.it/. [Accessed 07-15-2020].Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Other conferences
    MLMI '20: Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence
    September 2020
    138 pages
    ISBN:9781450388344
    DOI:10.1145/3426826

    Copyright © 2020 ACM

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    New York, NY, United States

    Publication History

    • Published: 17 December 2020

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