Parking Occupancy Detection Using Convolutional Neural Networks
DOI:
https://doi.org/10.26438/ijcse/v6i10.272275Keywords:
Convolution neural networks,, Parking lot, Vacancy detectionAbstract
Sophisticated world has the gifted man not only with comforts but also with many problems, one of the unavoidable and the most challenging problem is vehicle parking problem. The unregulated parking system is leading to huge traffic and accidents. Parking the vehicle in the parking space is highly unorganized and people have to manually check for the vacant places for parking their vehicles. So most of the people will park their vehicles in empty spaces or on the road which increases the problem further. In recent years, a lot of papers have been published addressing this issue. However, implementing them is highly expensive due to their usage of the costly sensor technology and other hardware requirements. But this paper proposes an intelligent parking system for vacancy detection using convolution neural networks that give accurate results under any Circumstances.
References
[1] N. Dan, "Parking management system and method" Jan. 2002, US Patent App. 10/066,215.
[2] Q. Wu, C. Huang, S.-y.Wang, W-c.Chiu, and T. Chen, "Robust parking space detection considering inter-space correlation" in Multimedia and Expo, IEEE International Conference on. IEEE, 2007, pp. 659–662.
[3] C. G. del Postigo, J. Torres, and J. M. Menendez, "Vacant parking area estimation through background subtraction and transience map analysis" IET Intelligent Transport Systems, 2015
[4] P. R. de Almeida, L. S. Oliveira, A. S. Britto, E. J. Silva, and A. L. Koerich, "Plot–a robust dataset for parking lot classification "Expert Systems with Applications, vol. 42, no. 11, pp. 4937–4949, 2015"
[5] Toshimitsu Tanaka "Locating vehicles in the parking lot by image processing". Dec 11-13, 2002, Japan.
[6] Y. Bengio," Learning the deep architectures for AI" Foundations and trends in Machine Learning, vol. 2, no. 1, pp. 1–127, 2009"
[7] Jordan Cazamias and Martina Mark "Parking space classification using convolution neural networks".
[8] Fabio Carrara and Claudio Gennaro "Car parking occupancy detection using smart cameras and deep learning"
[9] SepehrValipour, Mennatullah Siam, EleniStroulia, Martin Jagersand "parking stall vacancy indicator system"
[10] R. Yusnita, FarizaNorbaya, and NorazwinawatiBasharuddin "Intelligent parking space detection based on image processing" International Journal of Innovation, Management and Technology, Vol. 3, No. 3, June 2012
[11] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Image net classification with deep convolution neural networks," in Advances in neural information processing systems, 2012, pp. 1097–1105.
[12] K. Simonyan and A. Zisserman, "Very deep convolution networks for large-scale image recognition" Vol. 3, No. 3, 2014.
[13] R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," in Computer Vision and Pattern Recognition, 2014.
[14] KeironTeilo O`Shea "An introduction to Convolutional neural networks". Vol. 2, Dec 2015
[15] Almeida et al. (2013)Almeida, Oliveira, Silva, BrittoJr&Koerich] Almeida, P., Oliveira, L. S., Silva, E., BrittoJr, A. S., &Koerich, A. (2013). Parking space detection using textural descriptors.In Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on (pp. 3603_3608). doi:10.1109/SMC.2013.614.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
