Fast and Stable Human Detection Using Multiple Classifiers Based on Subtraction Stereo with HOG Features

被引：0

作者：

Arie, Makoto ^{[1
]}

Moro, Alessandro ^{[2
]}

Hoshikawa, Yuma ^{[1
]}

Ubukata, Toru ^{[1
]}

Terabayashi, Kenji ^{[3
]}

Umeda, Kazunori ^{[3
]}

机构：

[1] Chuo Univ, Sch Sci & Engn, Course Precis Engn, CREST,JST,Bunkyo Ku, Tokyo 1128551, Japan

[2] Univ Trieste, CREST, JST, Dept Ind & Informat Engn, Trieste, Italy

[3] Chuo Univ, CREST JST, Fac Sci & Engn, Dept Precis Mech,Bunkyo Ku, Tokyo, Japan

来源：

2011 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) | 2011年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we propose a fast and stable human detection based on "subtraction stereo" which can measure distance information of foreground regions. Scanning an input image by detection windows is controlled in their window sizes and number using the distance information obtained from subtraction stereo. This control can skip a large number of detection windows and leads to reduce the computational time and false detection for fast and stable human detection. Additionally, we propose two-step boosting as a new training way of classifier with whole and upper human body models. Experimental results show that the proposal is faster and less false detection than the method described in the reference [1].

引用

页码：868 / 873

页数：6

共 23 条

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