Gradient Local Binary Patterns for human detection

Ning Jiang, Jiu Xu, Wenxin Yu, Satoshi Goto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

38 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - IEEE International Symposium on Circuits and Systems
Number of pages4
Publication statusPublished - 2013
Event2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013 - Beijing
Duration: 2013 May 192013 May 23


Other2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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