Pedestrian Detection Using Gradient Local Binary Patterns

Ning JIANG  Jiu XU  Satoshi GOTO  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E95-A   No.8   pp.1280-1287
Publication Date: 2012/08/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E95.A.1280
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Image Media Quality)
Category: Coding & Processing
pedestrian detection,  feature extraction,  local binary pattern,  support vector machine,  gradient local binary pattern,  

Full Text: PDF(3.2MB)>>
Buy this Article

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.