Abstract
We propose a pattern recognition algorithm that uses computer vision to analyze and map the location of parking spaces from aerial images of parking lots. The analysis method developed made use of line detection coupled with selective filtering based on the prevalence of line length and angle. The goal of this algorithm was to provide a means of automated detection of regions of interest in parking lot images for further use in collection of parking data. Aerial images that used for development and testing were collected via a quad-copter. The quad-copter was equipped with a camera mounted via a gimbal that maintained a camera angle parallel to the parking lot surface. Video was collected from an altitude of 400 ft and individual frames were selected for content. The images were then split into a development set and a testing set. For analysis, images were converted to gray-scale followed by the application of a binary filter. Line features in the binary images were then detected using a Hough transform. Resulting features were then analyzed iteratively to find recurring line patterns of similar length and angle. After filtering for noise, line end-points and intersections were grouped to estimate individual parking space locations. We performed analysis of the proposed methodology over real spaces and demonstrate good performance results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Amato, G., Carrara, F., Falchi, F., Gennaro, C., Vairo, C.: Car parking occupancy detection using smart camera networks and deep learning. In: 2016 IEEE Symposium on Computers and Communication (ISCC), pp. 1212–1217, June 2016
Chen, Y.-L., Wu, B.-F., Fan, C.-J..: Real-time vision-based multiple vehicle detection and tracking for nighttime traffic surveillance. In: Proceedings of the 2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009, pp. 3352–3358, Piscataway, NJ, USA. IEEE Press (2009)
Delibaltov, D., Wu, W., Loce, R.P., Bernal, E.A.: Parking lot occupancy determination from lamp-post camera images. In: 2013 16th International IEEE Conference on Intelligent Transportation Systems-(ITSC), pp. 2387–2392. IEEE (2013)
Fatsi, E.: Parking space detection with OpenCV and a raspberry Pi (2014)
Huang, C.C., Wang, S.J.: A hierarchical bayesian generation framework for vacant parking space detection. IEEE Trans. Circuits. Syst. Video Technol. 20(12), 1770–1785 (2010)
Illingworth, J., Kittler, J.: A survey of the hough transform. Comput. Vis. Graph. image Process. 44(1), 87–116 (1988)
Jensen, T.H.P., Schmidt, H.T., Bodin, N.D., Nasrollahi, K., Moeslund, T.B.: Parking space occupancy verification - improving robustness using a convolutional neural network. In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017), pp. 311–318. INSTICC, SciTePress (2017)
Peng, C., Hsieh, J., Leu, S., Chuang, C.: Drone-based vacant parking space detection. In: 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 618–622 (2018)
True, N.: Vacant parking space detection in static images. University of California, San Diego, 17 (2007)
Wu, Q., Huang, C., Wang, S., Chiu, W., Chen, T.: Robust parking space detection considering inter-space correlation. In: 2007 IEEE International Conference on Multimedia and Expo, pp. 659–662, July 2007
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Regester, A., Paruchuri, V. (2020). Using Computer Vision Techniques for Parking Space Detection in Aerial Imagery. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-17798-0_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17797-3
Online ISBN: 978-3-030-17798-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)