Image-Based Approach for Parking-Spot Detection with Occlusion Handling
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 9
Abstract
With the aid of advanced information technology, car parking space management is evolving dramatically toward an automatic way. The most efficient approach for parking-spot detection is based on embedded sensors, which can cause a significant cost of equipment purchasing, installation, and maintenance. Therefore, a growing number of studies have been done on vision-based detection methods using cameras. This paper aims to develop a parking-spot detection method based on images captured by existing surveillance cameras at car parks. Such images are used for recognition of parking lines, parking-spot positioning, and vehicle feature extraction. The issue of vehicle occlusion due to the limited installation height of surveillance cameras in car parks is handled. With the proposed detection approach, vacant and occupied parking spots could be distinguished to provide useful information of parking-space statuses to drivers. Various experiments have been conducted with promising results in different environmental conditions, like daytime and evening, sunny and rainy days, indoor and outdoor, and low and high camera positions. The proposed approach is applicable to large-scale car parks based on an extended multicamera system.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request, including sample images of parking spots and selected code used in this research.
References
Alam, M., D. Moroni, G. Pieri, M. Tampucci, M. Gomes, J. Fonseca, and G. R. Leone. 2018. “Real-time smart parking systems integration in distributed ITS for smart cities.” J. Adv. Transp. 2018: 1485652. https://doi.org/10.1155/2018/1485652.
Amato, G., F. Carrara, F. Falchi, C. Gennaro, C. Meghini, and C. Vairo. 2017. “Deep learning for decentralized parking lot occupancy detection.” Expert Syst. Appl. 72 (Apr): 327–334. https://doi.org/10.1016/j.eswa.2016.10.055.
Bayraktar, M. E., F. Arif, H. Ozen, and G. Tuxen. 2015. “Smart parking-management system for commercial vehicle parking at public rest areas.” J. Transp. Eng. 141 (5): 04014094. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000756.
Blumer, K., H. R. Halaseh, M. U. Ahsan, H. Dong, and N. Mavridis. 2012. “Cost-effective single-camera multi-car parking monitoring and vacancy detection towards real-world parking statistics and real-time reporting.” In Proc., Int. Conf. on Neural Information, 506–515. Berlin: Springer.
Canny, J. 1986. “A computational approach to edge detection.” IEEE Trans. Pattern Anal. Mach. Intell. 8 (6): 679–698. https://doi.org/10.1109/TPAMI.1986.4767851.
Duda, R. O., and P. E. Hart. 1972. “Use of the Hough transformation to detect lines and curves in pictures.” Commun. ACM 15 (1): 11–15. https://doi.org/10.1145/361237.361242.
Haralick, R. M., S. R. Sternberg, and X. Zhuang. 1987. “Image analysis using mathematical morphology.” IEEE Trans. Pattern Anal. Mach. Intell. 9 (4): 532–550. https://doi.org/10.1109/TPAMI.1987.4767941.
Huang, C. C., Y. S. Dai, and S. J. Wang. 2012. “A surface-based vacant space detection for an intelligent parking lot.” In Proc., 12th Int. Conf. on ITS Telecommunications, 284–288. New York: IEEE.
Idris, M. Y. I., E. M. Tamil, N. M. Noor, Z. Razak, and K. W. Fong. 2009. “Parking guidance system utilizing wireless sensor network and ultrasonic sensor.” Inf. Technol. J. 8 (2): 138–146. https://doi.org/10.3923/itj.2009.138.146.
Kianpisheh, A., N. Mustaffa, P. Limtrairut, and P. Keikhosrokiani. 2012. “Smart parking system (SPS) architecture using ultrasonic detector.” Int. J. Software Eng. Appl. 6 (3): 55–58.
Lee, S., D. Yoon, and A. Ghosh. 2008. “Intelligent parking lot application using wireless sensor networks.” In Proc., Int. Symp. on Collaborative Technologies and Systems, 48–57. New York: IEEE.
Levin, M. W. 2019. “Linear program for system optimal parking reservation assignment.” J. Transp. Eng. Part A Syst. 145 (12): 04019049. https://doi.org/10.1061/JTEPBS.0000280.
Li, X., M. C. Chuah, and S. Bhattacharya. 2017. “UAV assisted smart parking solution.” In Proc., 2017 Int. Conf. on Unmanned Aircraft Systems, 1006–1013. New York: IEEE.
Liu, J., M. Mohandes, and M. Deriche. 2013. “A multi-classifier image based vacant parking detection system.” In Proc., 2013 IEEE 20th Int. Conf. on Electronics, Circuits, and Systems, 933–936. New York: IEEE.
Lopez, M., T. Griffin, K. Ellis, A. Enem, and C. Duhan. 2019. “Parking lot occupancy tracking through image processing.” In Proc., 34th Int. Conf. on Computers and Their Applications, 265–270. Manchester, UK: EPiC Series in Computing.
Maria, G., E. Baccaglini, D. Brevi, M. Gavelli, and R. Scopigno. 2016. “A drone-based image processing system for car detection in a smart transport infrastructure.” In Proc., 18th Mediterranean Electrotechnical Conf., 1–5. New York: IEEE.
Masmoudi, I., A. Wali, and A. M. Alimi. 2014. “Parking spaces modelling for inter spaces occlusion handling.” In Proc., 22nd WSCG Int. Conf. on Computer Graphics, Visualization and Computer Vision. Plzen, Czech Republic: Univ. of West Bohemia.
Nieto, R. M., Á. García-Martín, A. G. Hauptmann, and J. M. Martínez. 2019. “Automatic vacant parking places management system using multicamera vehicle detection.” IEEE Trans. Intell. Transp. Syst. 20 (3): 1069–1080. https://doi.org/10.1109/TITS.2018.2838128.
Peng, C. F., J. W. Hsieh, S. W. Leu, and C. H. Chuang. 2018. “Drone-based vacant parking space detection.” In Proc., 32nd Int. Conf. on Advanced Inf. Networking and Applications Workshops, 618–622. New York: IEEE.
True, N. 2007. Vol. 17 of Vacant parking space detection in static images. La Jolla, CA: Univ. of California, San Diego.
Valipour, S., M. Siam, E. Stroulia, and M. Jagersand. 2016. “Parking-stall vacancy indicator system, based on deep convolutional neural networks.” In Proc., 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), 655–660. New York: IEEE.
Xie, S., and Z. Tu. 2017. “Holistically-nested edge detection.” Int. J. Comput. Vision 125 (1–3): 3–18. https://doi.org/10.1007/s11263-017-1004-z.
Yusnita, R., F. Norbaya, and N. Basharuddin. 2012. “Intelligent parking space detection system based on image processing.” Int. J. Innovation Manage. Technol. 3 (3): 232–235.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 9 • September 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Nov 7, 2019
Accepted: Apr 24, 2020
Published online: Jul 8, 2020
Published in print: Sep 1, 2020
Discussion open until: Dec 8, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.