Vehicle Classification Based on Locational Matrix and Edge Region Minimization

DOI

10.2991/iccet-15.2015.374How to use a DOI?

Keywords

Locational matrix; edge; classification; grid partition; similarity probability

Abstract

Due to the influence of gray contrast ratio on background image with the target vehicle, traditional vehicle detection algorithms cannot adapt to complicated traffic environment. This paper proposes a new vehicle classification method based on locational matrix and minimum of local edge region. First, the lane line is determined by gray gradient variation and the regions surrounded by lane lines is regarded as the recognition region for further vehicle detection. Second, grid partition of the original image is conducted and classification label of each grid is determined by the assumed gray threshold. Then locational matrix is obtained based on the label number of the corresponding grids and the similarity rate is analyzed with different to get the optimal value. The relationship between the distance from the mean of local region gray to the connected domain of vehicle body and is the iteration termination condition. Finally, vehicle outline edge is refined through the minimum of edge local region. Test results show that our method classifies vehicle outline edge accurately, compared with single image match method.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Mingfang Zhang
AU  - Li Li
AU  - Yu Cui
PY  - 2015/11
DA  - 2015/11
TI  - Vehicle Classification Based on Locational Matrix and Edge Region Minimization
BT  - Proceedings of the 5th International Conference on Civil Engineering and Transportation 2015
PB  - Atlantis Press
SP  - 2000
EP  - 2003
SN  - 2352-5401
UR  - https://doi.org/10.2991/iccet-15.2015.374
DO  - 10.2991/iccet-15.2015.374
ID  - Zhang2015/11
ER  -