Pedestrian Detection Using Gradient Local Binary Patterns

Ning JIANG; Jiu XU; Satoshi GOTO

doi:10.1587/transfun.E95.A.1280

Pedestrian Detection Using Gradient Local Binary Patterns

Ning JIANG, Jiu XU, Satoshi GOTO

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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 named gradient local binary patterns (GLBP) for human detection. In this feature, original 256 local binary patterns are reduced to 56 patterns. These 56 patterns named uniform patterns are used for generating a 56-bin histogram. And gradient value of each pixel is set as the weight which is always same in LBP based features in histogram calculation to computing the values in 56 bins for histogram. Experiments are performed on INRIA dataset, which shows the proposal GLBP feature is discriminative than histogram of orientated gradient (HOG), Semantic Local Binary Patterns (S-LBP) and histogram of template (HOT). In our experiments, the window size is fixed. That means the performance can be improved by boosting 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 pedestrian detection.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E95-A No.8 pp.1280-1287

Publication Date: 2012/08/01

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Online ISSN: 1745-1337

DOI: 10.1587/transfun.E95.A.1280

Type of Manuscript: Special Section PAPER (Special Section on Image Media Quality)

Category: Coding & Processing

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Ning JIANG, Jiu XU, Satoshi GOTO, "Pedestrian Detection Using Gradient Local Binary Patterns" in IEICE TRANSACTIONS on Fundamentals, vol. E95-A, no. 8, pp. 1280-1287, August 2012, doi: 10.1587/transfun.E95.A.1280.
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 named gradient local binary patterns (GLBP) for human detection. In this feature, original 256 local binary patterns are reduced to 56 patterns. These 56 patterns named uniform patterns are used for generating a 56-bin histogram. And gradient value of each pixel is set as the weight which is always same in LBP based features in histogram calculation to computing the values in 56 bins for histogram. Experiments are performed on INRIA dataset, which shows the proposal GLBP feature is discriminative than histogram of orientated gradient (HOG), Semantic Local Binary Patterns (S-LBP) and histogram of template (HOT). In our experiments, the window size is fixed. That means the performance can be improved by boosting 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 pedestrian detection.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E95.A.1280/_p

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@ARTICLE{e95-a_8_1280,
author={Ning JIANG, Jiu XU, Satoshi GOTO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Pedestrian Detection Using Gradient Local Binary Patterns},
year={2012},
volume={E95-A},
number={8},
pages={1280-1287},
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 named gradient local binary patterns (GLBP) for human detection. In this feature, original 256 local binary patterns are reduced to 56 patterns. These 56 patterns named uniform patterns are used for generating a 56-bin histogram. And gradient value of each pixel is set as the weight which is always same in LBP based features in histogram calculation to computing the values in 56 bins for histogram. Experiments are performed on INRIA dataset, which shows the proposal GLBP feature is discriminative than histogram of orientated gradient (HOG), Semantic Local Binary Patterns (S-LBP) and histogram of template (HOT). In our experiments, the window size is fixed. That means the performance can be improved by boosting 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 pedestrian detection.},
keywords={},
doi={10.1587/transfun.E95.A.1280},
ISSN={1745-1337},
month={August},}

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TY - JOUR
TI - Pedestrian Detection Using Gradient Local Binary Patterns
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1280
EP - 1287
AU - Ning JIANG
AU - Jiu XU
AU - Satoshi GOTO
PY - 2012
DO - 10.1587/transfun.E95.A.1280
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E95-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2012
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 named gradient local binary patterns (GLBP) for human detection. In this feature, original 256 local binary patterns are reduced to 56 patterns. These 56 patterns named uniform patterns are used for generating a 56-bin histogram. And gradient value of each pixel is set as the weight which is always same in LBP based features in histogram calculation to computing the values in 56 bins for histogram. Experiments are performed on INRIA dataset, which shows the proposal GLBP feature is discriminative than histogram of orientated gradient (HOG), Semantic Local Binary Patterns (S-LBP) and histogram of template (HOT). In our experiments, the window size is fixed. That means the performance can be improved by boosting 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 pedestrian detection.
ER -