Application of Soft Histogram of Oriented Gradient on Traffic Sign Detection

被引:0
|
作者
Kassani, Peyman Hosseinzadeh [1 ]
Hyun, Junhyuk
Kim, Euntai [1 ]
机构
[1] Yonsei Univ, Dept Elect Engn, Seoul 120749, South Korea
关键词
Feature Extraction and Pattern Recognition; Learning and Adaptive Systems;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The proposal of this study is introducing a new feature, called soft Histogram of Oriented Gradients (SHOG). This feature is designed for traffic sign detection. SHOG differs from traditional (hard) HOG in terms of symmetry information and cell of histogram positions. Unlike hard HOG, SHOG changes the positions of cells to a randomized selection of cells following by symmetry shapes of the traffic sign images. SHOG is implemented on the famous German traffic sign detection benchmark (GTSDB) dataset. Comparing to the conventional HOG feature experimented on GTSDB, SHOG could show better performance while uses smaller feature size.
引用
收藏
页码:388 / 392
页数:5
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