Abstract
With the development of industrial automation, there is a growth in number of vehicles resulted to demand of parking space, which is costly in metropolitan areas. For finding vacant parking facilities, drivers spend more time on roads which causes additional fuel consumption, traffic congestion, and pollution. For addressing parking issues, this study presents a novel framework based on real-time monitoring and image recognition technique. The major contribution of this study is threefold: Initially, analyze the input images which are composed from the event recorders in currently driving cars toward determining the parking spaces availability. Second, this image is preprocessed using a hybrid algorithm, which is a combination of feature extraction (local binary pattern) and pattern recognition (Bayes classifier) technique. With the help of image recognition technique, it can track the availability of parking spaces with images on weather condition. Also, image filtering technique is applied to remove noisy information; hence, we can detect the park lots in any weather condition. Third, consider the utilization of parking facilities, distance to the recommended parking facility, and time to reach from source to destination. Finally, the performance of the suggested technique is validated by the measure of classification accuracy, precision, recall, and f-measure.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hussain, K, F., Afifi, M., Moussa, G.: ‘A Comprehensive Study of the Effect of Spatial Resolution and Color of Digital Images on Vehicle Classification’, IEEE Transactions on Intelligent Transportation Systems, 2018, 20, (3), pp. 1181–1190
M.W. Szeto, D.C. Gazis, Application of kalman filtering to the surveillance and control of traffic systems. Transp. Sci. 6(4), 419–439 (1972)
G. Vigos, M. Papageorgiou, Y. Wang, Real-time estimation of vehicle-count within signalized links. Transp. Res. Part C Emerg. Technol. 16(1), 18–35 (2008)
S. Deshpande, M-parking: Vehicle Parking Guidance System Using Hierarchical Wireless Sensor Networks (Las Vegas, USA, January, IEEE Annual Consumer Communications & Networking Conf., 2016), pp. 808–811
H.V. Chand, J. Karthikeyan, Survey on the role of IoT in intelligent transportation system. Indonesian J. Electr. Eng. Comput. Sci. 11(3), 936–941 (2018)
X. Clady, P. Negri, M. Milgram, Multi-class vehicle type recognition system. Proc. IAPR Workshop on Artificial Neural Networks in Pattern Recognition, (Paris, France, July 2008), pp. 228–239
M. Betke, E. Haritaoglu, L.S. Davis, Real-time multiple vehicle detection and tracking from a moving vehicle. Mach. Vis. Appl. 12(2), 69–83 (2000)
S.G. Kashid, S.A. Pardeshi, Detection and identification of illegally parked vehicles at no parking area. (Melmaruvathur, India, Int. Conf. Commun. Signal Process, 2014) pp. 1025–1029
A. Kumar, M.H. Assaf, S.R. Das, et al. Image processing based system for classification of vehicles for parking purposes. Proc. Int. Instrum. Measur. Technol. Conf., Pisa, Italy, 1326–1330 (May 2015)
Q.G.K. Safi, S. Luo, L. Pan, et al SVPS: cloud-based smart vehicle parking system over ubiquitous VANETs. Comput. Netw. 138, 18–30 (2018)
S. Rane, A. Dubey, T. Parida, Design of IoT based intelligent parking system using image processing algorithms (Int. Conf. on Computing Methodologies and Communication, Erode, India, 2017), pp. 1049–1053
C.F. Yang, Y.H. Ju, C.Y. Hsieh et al., iParking—a Real-Time Parking Space Monitoring and Guiding System, vol 9 (Vehicular Communications, 2017), pp. 301–305
A. Ajay, V. Sowmya, K.P. Soman, Vehicle detection in aerial imagery using eigen features. Int. Conf. on Communication and Signal Processing. (Chennai, India, April 2017) pp. 1620–1624
H. Al-Kharusi, I. Al-Bahadly, Intelligent parking management system based on image processing. World J. Eng. Technol. 2(2), 55–67 (2014)
A. Ajay, K.D.M. Dixon, V. Sowmya, Aerial image classification using GURLS and LIBSVM. (Melmaruvathur, India, Int. Conf. on Communication and Signal Processing, 2016), pp. 0396–0401
N. Tawara, T. Ogawa, S. Watanabe et al., A sampling-based speaker clustering using utterance-oriented dirichlet process mixture model and its evaluation on large-scale data. APSIPA Trans. Signal Inf. Process. 4, 1–10 (2015)
R. Arandjelovic, A. Zisserman, All About VLAD (IEEE Conf. on Computer Vision and Pattern Recognition, Portland, USA, June, 2013), pp. 1578–1585
H. Jegou, M. Douze, C. Schmid et al., Aggregating local descriptors into a compact image representation (IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, San Francisco, USA, 2010), pp. 3304–3311
J. Sanchez, F. Perronnin, T. Mensink et al., Image classification with the fisher vector: theory and practice. Int. J. Comput. Vis. 105(3), 222–245 (2013)
K. He, X. Zhang, S. Ren, Deep Residual Learning for Image Recognition (IEEE Conf. on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016), pp. 770–778
K.F. Hussain, M. Afifi, G. Moussa, A comprehensive study of the effect of spatial resolution and color of digital images on vehicle classification. IEEE Trans. Intell. Transp. Sys. 20(3), 1181–1190 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Varun Chand, H., Karthikeyan, J. (2021). Design and Implementation of Parking System Using Feature Extraction and Pattern Recognition Technique. In: Peter, J., Fernandes, S., Alavi, A. (eds) Intelligence in Big Data Technologies—Beyond the Hype. Advances in Intelligent Systems and Computing, vol 1167. Springer, Singapore. https://doi.org/10.1007/978-981-15-5285-4_39
Download citation
DOI: https://doi.org/10.1007/978-981-15-5285-4_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5284-7
Online ISBN: 978-981-15-5285-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)