18
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Detection of vacant slots in parking area through machine learning techniques using optimized ensemble classification model

, , &

References

  • P. Almeida, L.S. Oliveira, E. Silva, A.S. Britto and A.L. Koerich, “Parking space detection using textural descriptors”, In Systems, Man, and Cybernetics (SMC), IEEE International Conference, pp. 3603-3608, 2013.
  • P. Almeida, L.S. Oliveira, E. Silva, A.S. Britto, E.J. Silva and A.L. Koerich , “PKLot-A robust dataset for parking lot classification”, Expert Systems with Applications., Vol. 42, No. 11, pp. 4937–4949, 2015. doi: https://doi.org/10.1016/j.eswa.2015.02.009
  • M. Ahrnbom, K. Astrom, and M. Nilsson, “Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features”, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1609–1615, 2016.
  • Y. W. Seo and C. Urmson, “Utilizing prior information to enhance self-supervised aerial image analysis for extracting parking lot structures”, in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 339-344, 2009
  • Y. Geng and C.G. Cassandras, “A New ‘Smart Parking’ System Based on Optimal Resource Allocation and Reservations”, International IEEE Conference on Intelligent Transportation Systems Washington, DC, USA, Vol. 14, 2011.
  • C. Tang, X. Wei, C. Zhu, W. Chen, and J.J.P.C. Rodrigues, “Towards smart parking based on fog computing,” IEEE Access, Vol. 6, pp. 70172–70185, 2018. doi: https://doi.org/10.1109/ACCESS.2018.2880972
  • C. Badii, P. Nesi and I. Paoli, “Predicting Available Parking Slots on Critical and Regular Services by Exploiting a Range of Open Data,” IEEE Access, Vol. 6, pp. 44059–44071, 2018. doi: https://doi.org/10.1109/ACCESS.2018.2864157
  • A.O. Kotb, Y.C. Shen, and Y. Huang, “Smart Parking Guidance, Monitoring and Reservations: A Review”, InIEEE Intelligent Transportation Systems Magazine, Vol. 9, No. 2, pp. 6–16, 2017. doi: https://doi.org/10.1109/MITS.2017.2666586
  • Q. G. K. Safi, S. Luo, L. Pan, W. Liu, R. Hussain, and S. H. Bouk, “SVPS: Cloud-based smart vehicle parking system over ubiquitous VANETs”, Computer Networks, Vol. 138, pp. 18–30, 2018. doi: https://doi.org/10.1016/j.comnet.2018.03.034
  • C. C. Huang and S. J. Wang, “A Hierarchical Bayesian Generation Framework for Vacant Parking Space Detection”, InIEEE Transactions on Circuits and Systems for Video Technology, Vol. 20, No. 12, pp. 1770– 1785, 2011. doi: https://doi.org/10.1109/TCSVT.2010.2087510
  • S. Funck, N. Mohler, and W. Oertel, “Determining car-park occupancy from single images”, InProceedings of IEEE Intelligent Vehicles Symposium, pp. 325–328, 2004.
  • C. H. Lee, M. G. Wen, C. C. Han, and D. C. Kou, “An automatic monitoring approach for unsupervised parking lots in outdoors”, InProceedings of International Carnahan Conference on Security Technology, pp. 271–274, 2005.
  • T. Horprasert, D. Harwood, and L.S. Davis, “A statistical approach for real-time robust background subtraction and shadow detection”, InIEEE ICCV., 1999.
  • C.C. Huang, Y.S. Tai and S.J. Wang, “Vacant parking space detection based on plane-based bayesian hierarchical framework”, InIEEE Transactions on Circuits and Systems for Video Technology, Vol. 23, No. 9, pp. 1598–1610, 2013. doi: https://doi.org/10.1109/TCSVT.2013.2254961
  • I. Masmoudi, A. Wali and A.M. Alimi, “Parking Spaces Modelling for Inter Spaces Occlusion Handling”, International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, pp. 119-124. 2014.
  • J. Jermsurawong, U. Ahsan, A. Haidar, H. Dong and N. Mavridis, “One-day long statistical analysis of parking demand by using single-camera vacancy detection”, Journal of Transportation Systems Engineering and Information Technology, Vol. 14, No. 2, pp. 33–44, 2014. doi: https://doi.org/10.1016/S1570-6672(13)60136-1
  • J. Yue, Z. Li, L. Liu, Z. Fu, “Content based image retrieval using color and texture fused features”, Mathematical and Computer Modelling, Elsevier, Vol. 54, Issues 3–4, pp. 1121-1127, 2011. doi: https://doi.org/10.1016/j.mcm.2010.11.044
  • X. Wang, X. Bai, W. Liu and L. Latecki, “Feature context for image classification and object detection”, Proceedings of IEEE International Conference of Computer Vision and Pattern Recognition, pp. 961–968, 2011.
  • S. A. Aoudia, R. Mahiou and B. Benzaid., “YACBIR: Yet Another Content Based Image Retrieval system”, IEEE International Conference on Information Visualization, pp. 570-575, 2010.
  • B. Sharma and Venugopalan, “Comparison of Neural Network Training Functions for Hematoma Classification in Brain CT Images”, IOSR Journal of Computer Engineering, Vol. 16 pp. 31-35, 2014. doi: https://doi.org/10.9790/0661-16123135
  • T. K. Mishra, B. Majhi and R. Dash, “ A Contour Descriptors-Based Generalized Scheme for Handwritten Odia Numerals Recognition”, Journal of Information Processing Systems, pp. 1-11, 2014.
  • S. Ramaswamy, R. Rastogi and K. Shim, “Efficient Algorithms for Mining Outliers from Large Data Sets”, ACM SIGMOD Record, Vol. 29, pp. 427-438, 2000. doi: https://doi.org/10.1145/335191.335437
  • Marc Boulle, “Compression-Based Averaging of Selective Naive Bayes Classifiers”, Journal of Machine Learning Research, Vol. 8, pp. 1659-1685, 2007.
  • S. Taheri and M. Mammadov, “Learning the naive Bayes classifier with optimization models”, International Journal of Applied Mathematics and Computer Science, Vol. 23, No. 4, pp. 787-795, 2013. doi: https://doi.org/10.2478/amcs-2013-0059
  • R. E. Fan, P. H. Chen and C. J. Lin. “Working set selection using second order information for training support vector machines”, Journal of Machine Learning Research, Vol. 6, pp. 1889–1918, 2005.
  • T. Hastie, R. Tibshirani and J. Friedman, “The Elements of Statistical Learning”, Second Edition. New-York: Springer, 2008.
  • Rahul, Priyansh Kedia, Subrat Sarangi and Monika, “Analysis of machine learning models for malware detection”, https://doi.org/https://doi.org/10.1080/09720529.2020.1721870, Journal of Discrete Mathematical Sciences and Cryptography
  • Akash Rajak, Ajay Kumar Shrivastava and Vidushi, “Applying and comparing machine learning classification algorithms for predicting the results of students”, https://doi.org/https://doi.org/10.1080/09720529.2020.1728895, Journal of Discrete Mathematical Sciences and Cryptography
  • Vanita Jain, Gopal Chaudhary, Nalin Luthra, Akshit Rao and Shlok Walia, “Dynamic handwritten signature and machine learning based identity verification for keyless cryptocurrency transactions”, https://doi.org/https://doi.org/10.1080/09720529.2019.1582867, Journal of Discrete Mathematical Sciences and Cryptography.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.