Skip to main content
Log in

GPSD: generative parking spot detection using multi-clue recovery model

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Due to various complex environmental factors and parking scenes, there are more stringent requirements for automatic parking than the manual one. The existing auto-parking technology is based on space or plane dimension, where the former usually ignores the ground parking spot lines which may cause parking at a wrong position, while the latter often costs a lot of time in object classification which may decreases the algorithm applicability. In this paper, we propose a Generative Parking Spot Detection algorithm which uses a multi-clue recovery model to reconstruct parking spots. In the proposed method, we firstly dismantle the parking spot geometrically for marking the location of its corresponding corners and then use a micro-target recognition network to find corners from the ground image taken by car cameras. After these, we use the multi-clue model to correct the fully pairing map so that the reliable true parking spot can be recovered correctly. The proposed algorithm is compared with several existing algorithms, and the experimental result shows that it has a higher accuracy than others which can reach more than 80% in most test cases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Athira, A., Lekshmi, S., Vijayan, P., Kurian, B.: Smart parking system based on optical character recognition. In: 3rd International Conference on Trends in Electronics and Informatics, pp. 1184–1188 (2019)

  2. Bacchiani, G., Patander, M., Cionini, A., Giaquinto, D.: Parking slots detection on the equivalence sphere with a progressive probabilistic Hough transform. In: IEEE 20th International Conference on Intelligent Transportation Systems, pp. 1–6 (2017)

  3. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

  4. Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: Yolact: Real-time instance segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9157–9166 (2019)

  5. Dixit, M., Srimathi, C., Doss, R., Loke, S., Saleemdurai, M.: Smart parking with computer vision and iot technology. In: 43rd International Conference on Telecommunications and Signal Processing, pp. 170–174 (2020)

  6. Hakim, I.M., Christover, D., Marindra, A.M.J.: Implementation of an image processing based smart parking system using Haar-cascade method. In: IEEE 9th Symposium on Computer Applications & Industrial Electronics (ISCAIE), pp. 222–227 (2019)

  7. Hamada, K., Hu, Z., Fan, M., Chen, H.: Surround view based parking lot detection and tracking. In: IEEE Intelligent Vehicles Symposium (IV), pp. 1106–1111 (2015)

  8. Huang, J., Zhang, L., Shen, Y., Zhang, H., Zhao, S., Yang, Y.: Dmpr-ps: a novel approach for parking-slot detection using directional marking-point regression. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 212–217 (2019)

  9. Jian, D.H., Lin, C.H.: Vision-based parking slot detection based on end-to-end semantic segmentation training. In: IEEE International Conference on Consumer Electronics, pp. 1–4 (2020)

  10. Lee, M., Kim, S., Lim, W., Sunwoo, M.: Probabilistic occupancy filter for parking slot marker detection in an autonomous parking system using AVM. IEEE Trans. Intell. Transp. Syst. 20(6), 2389–2394 (2018)

    Article  Google Scholar 

  11. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

  12. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: single shot multibox detector. In: European Conference on Computer Vision, pp. 21–37. Springer (2016)

  13. Panzani, G., Nava, D., Savaresi, S.M.: An odometry free automatic perpendicular parking strategy for a light urban vehicle based on a low resolution lidar. In: IEEE Intelligent Transportation Systems Conference, pp. 2772–2777 (2019)

  14. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

  15. Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)

  16. Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)

  17. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)

  18. Scheunert, U., Fardi, B., Mattern, N., Wanielik, G., Keppeler, N.: Free space determination for parking slots using a 3d pmd sensor. In: 2007 IEEE Intelligent Vehicles Symposium, pp. 154–159 (2007)

  19. Sedighi, S., Nguyen, D.V., Kuhnert, K.D.: Implementation of a parking state machine on vision-based auto parking systems for perpendicular parking scenarios. In: 6th International Conference on Control, Decision and Information Technologies, pp. 1711–1716 (2019)

  20. Singh, T., Khan, S.S., Chadokar, S.: A review on automatic parking space occupancy detection. In: International Conference on Advanced Computation and Telecommunication, pp. 1–5 (2018)

  21. Song, J., Zhang, W., Wu, X., Cao, H., Gao, Q., Luo, S.: Laser-based slam automatic parallel parking path planning and tracking for passenger vehicle. IET Intell. Transp. Syst. 13(10), 1557–1568 (2019)

    Article  Google Scholar 

  22. Suhr, J., Jung, H.: Sensor fusion-based precise obstacle localisation for automatic parking systems. Electron. Lett. 54(7), 445–447 (2018)

    Article  Google Scholar 

  23. Suhr, J.K., Jung, H.G.: Full-automatic recognition of various parking slot markings using a hierarchical tree structure. Opt. Eng. 52(3), 037,203 (2013)

    Article  Google Scholar 

  24. Suhr, J.K., Jung, H.G.: End-to-end trainable one-stage parking slot detection integrating global and local information. IEEE Trans. Intell. Transp. Syst. 6, 66 (2021)

    Google Scholar 

  25. Suhr, J.K., Jung, H.G., Bae, K., Kim, J.: Automatic free parking space detection by using motion stereo-based 3d reconstruction. Mach. Vis. Appl. 21(2), 163–176 (2010)

    Article  Google Scholar 

  26. Unger, C., Wahl, E., Ilic, S.: Parking assistance using dense motion-stereo. Mach. Vis. Appl. 25(3), 561–581 (2014)

    Article  Google Scholar 

  27. Wu, Z., Sun, W., Wang, M., Wang, X., Ding, L., Wang, F.: Psdet: efficient and universal parking slot detection. In: IEEE Intelligent Vehicles Symposium (IV), pp. 290–297 (2020)

  28. Yamamoto, K., Watanabe, K., Nagai, I.: Proposal of an environmental recognition method for automatic parking by an image-based CNN. In: IEEE International Conference on Mechatronics and Automation, pp. 833–838 (2019)

  29. Ye, C., Chen, G., Qu, S., Yang, Q., Chen, K., Du, J., Hu, R.: Self-localization of parking robots using square-like landmarks. In: IEEE International Conference on Robotics and Biomimetics, pp. 1987–1992 (2018)

  30. Zhang, L., Huang, J., Li, X., Xiong, L.: Vision-based parking-slot detection: a dcnn-based approach and a large-scale benchmark dataset. IEEE Trans. Image Process. 27(11), 5350–5364 (2018)

    Article  MathSciNet  Google Scholar 

  31. Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.Z.: Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. arXiv preprint arXiv:1912.02424 (2019)

  32. Zinelli, A., Musto, L., Pizzati, F.: A deep-learning approach for parking slot detection on surround-view images. In: IEEE Intelligent Vehicles Symposium, pp. 683–688 (2019)

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61672228, 62077037, 61872241, 62072449 and 61632003, in part by the Shanghai Automotive Industry Science and Technology Development Foundation under Grant 1837, in part by the Science and Technology Commission of Shanghai Municipality under Grants 18410750700 and 17411952600, and in part by The Hong Kong Polytechnic University under Grants P0030419, P0030929 and P0035358.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Bin Sheng or Enhua Wu.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Z., Qiu, J., Sheng, B. et al. GPSD: generative parking spot detection using multi-clue recovery model. Vis Comput 37, 2657–2669 (2021). https://doi.org/10.1007/s00371-021-02199-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-021-02199-y

Keywords

Navigation