Name
Abstract | ||
|---|---|---|
Being fast to compute and simple to implement, Local Binary Pattern (LBP) has also shown superior performance in texture classification and face detection. However, it is not well optimized for pedestrian detection. At night/dark environment, pedestrian detection typically needs to overcome problems of low contrast, image blur, and image noise. A novel feature extraction method, consisting of Weighted LBP, Multi-resolution LBP, and Multi-scale LBP, is proposed to solve them. Experimental results show that the proposed method improves upon the basic LBP significantly and outperforms benchmarks such as HOG and CoHOG. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICIP.2011.6115883 | ICIP |
Keywords | Field | DocType |
face detection,pedestrians,night-dark environment,night/dark environment,local binary pattern feature extraction method,pedestrian detection,image resolution,texture classification,weighted lbp,image noise,image denoising,image restoration,multiresolution lbp,image recognition,feature extraction,image classification,image blur,local binary pattern (lbp),multi-resolution,image texture,multiscale lbp,multi-scale,local binary pattern,histograms,noise,edge detection | Computer vision,Pattern recognition,Image texture,Computer science,Local binary patterns,Image noise,Feature extraction,Artificial intelligence,Image restoration,Face detection,Contextual image classification,Pedestrian detection | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4577-1302-6 | 978-1-4577-1302-6 | 6 |
PageRank | References | Authors |
0.52 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunyun Cao | 1 | 7 | 1.91 |
| Sugiri Pranata | 2 | 36 | 5.78 |
| Hirofumi Nishimura | 3 | 9 | 2.11 |