Skip to main content
Log in

An improved parking space recognition algorithm based on panoramic vision

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In order to reduce parking difficulties caused by small parking spaces, low driver driving experience, and complex parking environment, parking assistance systems have attracted attention, but the identification of parking spaces is a key problem and technical difficulty. In this paper, an improved parking space recognition algorithm based on panoramic vision is proposed. Firstly, in the look-around image forming part, a method of distortion correction (DC) and perspective transformation (PT) based on LUT (Look Up Table) transformation is proposed to improve the processing speed of the algorithm. Then, to improve the accuracy of parking space recognition, an improved method combining rough extraction and fine matching is proposed to identify parking spaces in a look-around image. The experimental results show that the method achieves a detection rate of 97.63% under sufficient illumination and 79.77% even under insufficient illumination.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Bay H, Tuytelaars T, Gool L Van (2006) SURF: Speeded Up Robust Features. In: Computer Vision – ECCV 404–417. https://doi.org/10.1016/j.cviu.2007.09.014

  2. Haralick RM (1984) A Fast parallel algorithm for thinning digital patterns. Pattern Recogn Lett 27:236–239. https://doi.org/10.1145/357994.358023

    Article  Google Scholar 

  3. Harris C, Stephens M (2013) A combined corner and edge detector. In: Proceedings of the Alvey Vision Conference, pp 147–151. https://doi.org/10.5244/c.2.23

  4. Heikkilä J, Silvén O (1997) A four-step camera calibration procedure with implicit image correction. In: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, pp 1106–1112. https://doi.org/10.1109/CVPR.1997.609468

  5. Kuo Y, Pai N, Li Y (2011) Vision-based vehicle detection for a driver assistance system. Comput Math Appl 61:2096–2100. https://doi.org/10.1016/j.camwa.2010.08.081

    Article  Google Scholar 

  6. Li L, Zhang L, Li X, Liu X, Shen Y, Xiong L (2017) Vision-based parking-slot detection : A benchmark and a learning-based approach school of software engineering, Tongji university , shanghai , china collaborative innovation center of intelligent new energy vehicle , Tongji university , shanghai, china. 649–654. https://doi.org/10.1109/ICME.2017.8019419

  7. Liao X, Li K, Yin J (2017) Separable data hiding in encrypted image based on compressive sensing and discrete fourier transform. Multimed Tools Appl 76:20739–20753. https://doi.org/10.1007/s11042-016-3971-4

    Article  Google Scholar 

  8. Liao X, Guo S, Yin J, Wang H, Li X, Sangaiah AK (2018) New cubic reference table based image steganography. Multimed Tools Appl 77:10033–10050. https://doi.org/10.1007/s11042-017-4946-9

    Article  Google Scholar 

  9. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94

    Article  Google Scholar 

  10. Ministry of Public Security of the People’s Republic of China (2010) Code for setting of on-street parking spaces 2009-10-26. Stand Press, China, pp 1–8

    Google Scholar 

  11. Nobori K (2017) A surround view image generation method with low distortion for vehicle camera systems using a composite projection. In: 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), pp 386–389. https://doi.org/10.23919/MVA.2017.7986882

  12. Ostendorf B, Retallack AE (2019) Current understanding of the effects of congestion on traffic accidents. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph16183400

  13. Pan J, Appia V, Bovik AC (2016) Virtual top-view camera calibration for accurate object representation. In: Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, pp 21–24.. https://doi.org/10.1109/SSIAI.2016.7459165

  14. Peng L, Jianwei Z, Konghui G, Hu Z (2014) A parking-line detection method based on edge direction. In: Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC, pp105–108. https://doi.org/10.1109/SPAC.2014.6982666

  15. Perkovic N, Pranjkic M, Kolak I, Velikic G, Had R (2017) System for automotive machine vision. In:IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin, pp 243–245. https://doi.org/10.1109/ICCE-Berlin.2017.8210638

  16. Petrov P, Georgieva V (2018) Geometric path planning and tracking control with bounded steering angle for the parking problem of automatic vehicles geometric path planning and tracking control with bounded steering angle for the parking problem of automatic vehicles. AIP Conf Proc 2048:060017. https://doi.org/10.1063/1.5082132

    Article  Google Scholar 

  17. Rosten E, Porter R, Drummond T (2008) Faster and better : a machine learning approach to corner detection. IEEE Trans Pattern Anal Mach Intell 32(1):105–119 1–35. https://doi.org/10.1109/TPAMI.2008.275

    Article  Google Scholar 

  18. Scaramuzza D, Martinelli A, Siegwart R, Toolbox A, Calibrating E, Scaramuzza D, Martinelli A, Siegwart R (2009) A Toolbox for easily Calibrating omnidirectional cameras to cite this version: HAL id : inria-00359941 a Toolbox for easily Calibrating omnidirectional cameras. IROS. https://doi.org/10.3929/ethz-a-005656492

  19. Song Y, Liao C (2016) Analysis and review of state-of-the-art automatic parking assist system. IEEE Int Conf Veh Electron Saf 2016:1–6. https://doi.org/10.1109/ICVES.2016.7548171

    Article  Google Scholar 

  20. Suhr JK (2013) Full-automatic recognition of various parking slot markings using a hierarchical tree structure. Opt Eng 52(3):037203 52:1–14. https://doi.org/10.1117/1.OE.52.3.037203

    Article  MathSciNet  Google Scholar 

  21. Sun L, Liu D, Chen T, He M (2019) Road traffic safety: an analysis of the cross-effects of economic, road and population factors. Chinese J Traumatol 22:290–295. https://doi.org/10.1016/j.cjtee.2019.07.004

    Article  Google Scholar 

  22. Taki T, Machida M, Shimada R (2019) Trends of traffic fatalities and DNA analysis in traffic accident investigation. IATSS Res 43:84–89. https://doi.org/10.1016/j.iatssr.2019.05.001

    Article  Google Scholar 

  23. Velez G, Otaegui O (2017) Embedding vision-based advanced driver assistance systems : a survey. In: Its World Congress, 1–29. https://doi.org/10.1049/iet-its.2016.0026

  24. Wang Z, Bovik AC, Sheikh HR, Member S, Simoncelli EP, Member S (2004) Image quality assessment : from error visibility to structural similarity. IEEE Trans Image Process 13:1–14. https://doi.org/10.1109/TIP.2003.819861

    Article  Google Scholar 

  25. Wu T, Tsai P, Hu N, Chen J (2016) Research and implementation of auto parking system based on ultrasonic sensors. Int Conf Adv Mater Sci Eng 2016:643–645. https://doi.org/10.1109/ICAMSE.2016.7840267

    Article  Google Scholar 

  26. Xing Y, Lv C, Chen L, Wang H, Wang H, Cao D, Velenis E, Wang F (2018) Advances in Vision-Based Lane Detection : Algorithms , Integration , Assessment , and Perspectives on ACP-Based Parallel Vision. IEEE/CAA J Autom Sin 5:645–661. https://doi.org/10.1109/JAS.2018.7511063

    Article  Google Scholar 

  27. Yamamoto K, Watanabe K, Nagai I (2019) Proposal of an environmental recognition method for automatic parking by an image-based CNN. In: 2019 IEEE International Conference on Mechatronics and Automation (ICMA), pp833–838. https://doi.org/10.1109/ICMA.2019.8816556

  28. Yin X, Zhang J, Wu X, Huang J, Xu Y (2018) An improved lane departure warning algorithm based on fusion of F-Kalman filter and F-TLC. Multimed Tools Appl 78:12203–12222. https://doi.org/10.1007/s11042-018-6762-2

    Article  Google Scholar 

  29. Yu M, Ma G (2015) A visual parking guidance for surround view monitoring system. In: IEEE Intelligent Vehicles Symposium, Proceedings, 1–6. https://doi.org/10.1109/IVS.2015.7225662

  30. Zhang Z, Way OM (1999) Flexible camera calibration by viewing a plane from unknown orientations. In: Proceedings of the IEEE International Conference on Computer Vision, pp 0–7. https://doi.org/10.1109/ICCV.1999.791289

  31. Zhang J, Yin X, Luan J, Liu T (2019) An improved vehicle panoramic image generation algorithm. Multimed Tools Appl, 8(2):27663–27682. https://doi.org/10.1007/s11042-019-07890-w

Download references

Acknowledgments

This work is supported in part by the National Key Research and Development Program of China (2017YFB0102500), Natural Science Foundation of Jilin province (20170101133JC), the Korea Foundation for Advanced Studies’ International Scholar Exchange Fellowship for the academic year of 2017-2018, the Fundamental Research Funds for the Central Universities, and Jilin University (5157050847, 2017XYB252).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jindong Zhang.

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

Zhang, J., Liu, T., Yin, X. et al. An improved parking space recognition algorithm based on panoramic vision. Multimed Tools Appl 80, 18181–18209 (2021). https://doi.org/10.1007/s11042-020-10370-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-10370-1

Keywords

Navigation