Name
Abstract | ||
|---|---|---|
In this paper, we present a feature extraction approach for pedestrian detection by extracting the sparse representation of histograms of oriented gradients (HOG) feature and local binary pattern (LBP) feature using K-SVD. Moreover, we use PCA to reduce the dimension of HOG and LBP. We combine the low dimension principal features with the sparse representations of HOG feature directly for fast pedestrian detection from images. In addition, we compare the performance of sparse representations and PCA based features. Experimental results on INRIA databases show that the proposed approach provides a better detection result and spends less time. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-09333-8_78 | INTELLIGENT COMPUTING THEORY |
Keywords | Field | DocType |
Pedestrian Detection, Local Binary Patterns, Histogram of Oriented, Sparse Representation, K-SVD | Histogram,K-SVD,Pattern recognition,Computer science,Sparse approximation,Local binary patterns,Feature extraction,Artificial intelligence,Pedestrian detection | Conference |
Volume | ISSN | Citations |
8588 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen-Juan Pei | 1 | 1 | 0.35 |
| Yu-Lan Zhang | 2 | 1 | 0.35 |
| Y Zhang | 3 | 31 | 7.34 |
| Chun-hou Zheng | 4 | 732 | 71.79 |