Name
Abstract | ||
|---|---|---|
This paper presents a new descriptor, multi-sparse descriptor (MSD), for pedestrian detection in static images. Specifically, the proposed descriptor is based on multi-dictionary sparse coding which contains unsupervised dictionary learning and sparse coding. During unsupervised learning phase, a family of dictionaries with different representation abilities is learnt from the pedestrian data. Then the data are encoded by these dictionaries and the histogram of the sparse coefficients is calculated as the descriptor. The benefit of this multi-dictionary sparse encoding is three-fold: firstly, the dictionaries are learnt from the pedestrian data, they are more efficient for encoding local structures of the pedestrian; secondly, multiple dictionaries can enrich the representation by providing different levels of abstractions; thirdly, since the dictionaries based representation is mainly focused on the low frequency, better generalization ability along the scale range is obtained. Comparisons with the state-of-the-art methods reveal the superiority of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neucom.2015.07.143 | Neurocomputing |
Keywords | DocType | Volume |
Local descriptor,Sparse coding,Scale invariance,Pedestrian detection,Multi-dictionary learning | Journal | 184 |
ISSN | Citations | PageRank |
0925-2312 | 2 | 0.36 |
References | Authors | |
21 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yazhou Liu | 1 | 103 | 12.04 |
| Pongsak Lasang | 2 | 16 | 5.29 |
| Mel Siegel | 3 | 282 | 80.67 |
| Quansen Sun | 4 | 1222 | 83.09 |