Citation

Connie, Tee and Goh, Michael Kah Ong and Koo, Voon Chet and Murata, Ken T. and Phon-Amnuaisuk, Somnuk (2021) Improved Parking Space Recognition via Grassmannian Deep Stacking Network with Illumination Correction. In: 4th Computational Intelligence in Information Systems conference, CIIS 2021, 25-27 January 2021, Bandar Seri Begawan, Brunei Darussalam.

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Abstract

It has become increasingly difficult to quickly locate a free parking space with the growing number of private vehicles. Many parking space management solutions have been proposed. Vision-based methods are among the approaches that have received great attention due to the widespread use of surveillance cameras in parking areas. Although promising results have been reported for vision-based methods, these methods generally suffer when good quality images are not available. The performance of vision-based methods drops under conditions like low illumination. In this paper, an approach coined as Grassmannian Deep Stacking Network with Illumination Correction (GDSN-IC) is presented. The proposed method enhances the illumination map of an image before feeding it to a Grassmannian Deep Stacking Network for parking space availability prediction. Experiments on two public datasets validate the effectiveness of the proposed approach.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Grassmann manifolds
Subjects:	Q Science > QA Mathematics > QA440-699 Geometry. Trigonometry. Topology
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	05 Aug 2021 01:34
Last Modified:	05 Aug 2021 01:34
URII:	http://shdl.mmu.edu.my/id/eprint/9044

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