Vehicle Classification Based on Locational Matrix and Edge Region Minimization
- 10.2991/iccet-15.2015.374How to use a DOI?
- Locational matrix; edge; classification; grid partition; similarity probability
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.
- © 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/).
Cite this article
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 -