Gradient Local Binary Patterns for human detection

Ning Jiang, Jiu Xu, Wenxin Yu, Satoshi Goto

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

21 Citations (Scopus)

Abstract

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
Pages978-981
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013 - Beijing
Duration: 2013 May 192013 May 23

Other

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

Fingerprint

Object recognition
Face recognition
Textures
Experiments
Semantics
Hardware
Object detection

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Jiang, N., Xu, J., Yu, W., & Goto, S. (2013). Gradient Local Binary Patterns for human detection. In Proceedings - IEEE International Symposium on Circuits and Systems (pp. 978-981). [6572012] https://doi.org/10.1109/ISCAS.2013.6572012

Gradient Local Binary Patterns for human detection. / Jiang, Ning; Xu, Jiu; Yu, Wenxin; Goto, Satoshi.

Proceedings - IEEE International Symposium on Circuits and Systems. 2013. p. 978-981 6572012.

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

Jiang, N, Xu, J, Yu, W & Goto, S 2013, Gradient Local Binary Patterns for human detection. in Proceedings - IEEE International Symposium on Circuits and Systems., 6572012, pp. 978-981, 2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013, Beijing, 13/5/19. https://doi.org/10.1109/ISCAS.2013.6572012
Jiang N, Xu J, Yu W, Goto S. Gradient Local Binary Patterns for human detection. In Proceedings - IEEE International Symposium on Circuits and Systems. 2013. p. 978-981. 6572012 https://doi.org/10.1109/ISCAS.2013.6572012
Jiang, Ning ; Xu, Jiu ; Yu, Wenxin ; Goto, Satoshi. / Gradient Local Binary Patterns for human detection. Proceedings - IEEE International Symposium on Circuits and Systems. 2013. pp. 978-981
@inproceedings{84d3069f6add44779b238f866f65e802,
title = "Gradient Local Binary Patterns for human detection",
abstract = "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.",
author = "Ning Jiang and Jiu Xu and Wenxin Yu and Satoshi Goto",
year = "2013",
doi = "10.1109/ISCAS.2013.6572012",
language = "English",
isbn = "9781467357609",
pages = "978--981",
booktitle = "Proceedings - IEEE International Symposium on Circuits and Systems",

}

TY - GEN

T1 - Gradient Local Binary Patterns for human detection

AU - Jiang, Ning

AU - Xu, Jiu

AU - Yu, Wenxin

AU - Goto, Satoshi

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84883314620&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84883314620&partnerID=8YFLogxK

U2 - 10.1109/ISCAS.2013.6572012

DO - 10.1109/ISCAS.2013.6572012

M3 - Conference contribution

SN - 9781467357609

SP - 978

EP - 981

BT - Proceedings - IEEE International Symposium on Circuits and Systems

ER -