Name
Abstract | ||
|---|---|---|
Pedestrian detection remains a popular and challenging problem due to large variation in appearance. A robust feature extraction method is highly desired for accurate pedestrian detection. In this paper, firstly, we propose a staggered multiscale LBP histogram. In order to exploit grayscale difference information in more directions, three scales with radius of 1, 3, and 5 pixels are utilized, and different scales are staggered. The Staggered Multi-scale LBP histogram is composed of three 256-bin histograms, each of which corresponds to one of the three scales. Secondly, dimensionality of the LBP histogram is reduced using a boosting learning method. Experimental results show that the proposed feature outperforms benchmarks such as Uniform-LBP, HOG and CoHOG on INRIA, Daimler Chrysler and our Panasonic night time datasets. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICIP.2012.6466893 | ICIP |
Keywords | Field | DocType |
grayscale difference information,pedestrian detection,learning (artificial intelligence),staggered multiscale lbp histogram,boosting learning method,dimensionality reduction,feature extraction method,staggered multi-scale lbp,feature extraction,object detection,image colour analysis,boosting,learning artificial intelligence | Histogram,Computer vision,Object detection,Pattern recognition,Computer science,Feature (computer vision),Local binary patterns,Feature extraction,Boosting (machine learning),Artificial intelligence,Pedestrian detection,Grayscale | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4673-2532-5 | 978-1-4673-2532-5 | 0 |
PageRank | References | Authors |
0.34 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunyun Cao | 1 | 7 | 1.91 |
| Sugiri Pranata | 2 | 36 | 5.78 |
| Makoto Yasugi | 3 | 0 | 0.68 |
| Zhiheng Niu | 4 | 82 | 4.46 |
| Hirofumi Nishimura | 5 | 9 | 2.11 |