Pedestrian Detection using HOG, LUV and Optical Flow as Features with AdaBoost as Classifier

被引：0

作者：

Rauf, Rabia ^{[1
]}

Shahid, Ahmad R. ^{[1
]}

Ziauddin, Sheikh ^{[1
]}

Safi, Asad Ali ^{[1
]}

机构：

[1] COMSATS Inst Informat Technol, Dept Comp Sci, Islamabad, Pakistan

来源：

2016 SIXTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA) | 2016年

关键词：

Pedestrian Detection; AdaBoost; Random Forest; Decision Stump;

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：

Pedestrian detection has been used in applications such as car safety, video surveillance, and intelligent vehicles. In this paper, we present a pedestrian detection scheme using HOG, LUV and optical flow features with AdaBoost Decision Stump classifier. Our experiments on Caltech-USA pedestrian dataset show that the proposed scheme achieves promising results of about 16.7% log-average miss rate.

引用

页数：4

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