Gradient Local Binary Patterns for human detection

Ning Jiang, Jiu Xu, Wenxin Yu, Satoshi Goto

研究成果: Conference contribution

30 被引用数 (Scopus)

抄録

In recent years, local pattern based features have attracted increasing interest in object detection and recognition systems. Local Binary Pattern (LBP) feature is widely used in texture classification and face detection. But the original definition of LBP is not suitable for human detection. In this paper, we propose a novel feature set named gradient local binary patterns (GLBP), Original GLBP and Improved GLBP, for human detection. Experiments are performed on INRIA dataset, which shows the proposal GLBP feature is more discriminative than histogram of orientated gradient (HOG), histogram of template (HOT) and Semantic Local Binary Patterns (S-LBP), under the same training method. In our experiments, the window size is fixed. That means the performance can be improved by boosting and cascade methods. And the computation of GLBP feature is parallel, which make it easy for hardware acceleration. These factors make GLBP feature possible for real-time human detection.

本文言語English
ホスト出版物のタイトルProceedings - IEEE International Symposium on Circuits and Systems
ページ978-981
ページ数4
DOI
出版ステータスPublished - 2013
イベント2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013 - Beijing
継続期間: 2013 5月 192013 5月 23

Other

Other2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013
CityBeijing
Period13/5/1913/5/23

ASJC Scopus subject areas

  • 電子工学および電気工学

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