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

来源：

2016 13TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS AND AMBIENT INTELLIGENCE (URAI) | 2016年

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

页码：388 / 392

页数：5

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