Name
Abstract | ||
|---|---|---|
In this paper, we propose an efficient approach to moving pedestrian detection in video. This approach incorporates both motion and shape information and learns a codebook of shape context descriptors from a very small number of training samples. During the testing process, moving edgelets are firstly identified between adjacent frames using a local search method. Shape context descriptors for numerous sample points on identified edgelets are then produced and are matched against the instances of the learned codebook to generate initial hypotheses. The final hypotheses for pedestrians are obtained by pruning initial hypotheses. The proposed approach has the following advantages by comparison with the existing techniques: (1) lower computational cost, (2) lower false positive rate, and (3) fewer training samples. Experiments with a publicly available dataset confirm the performance of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1459359.1459488 | ACM Multimedia 2001 |
Keywords | Field | DocType |
efficient approach,shape information,pruning initial hypothesis,adjacent frame,initial hypothesis,fewer training sample,shape context descriptors,available dataset,training sample,local search,false positive rate,codebook | Small number,Computer vision,False positive rate,Pattern recognition,Computer science,Artificial intelligence,Local search (optimization),Shape context,Pedestrian detection,Machine learning,Codebook | Conference |
Citations | PageRank | References |
1 | 0.36 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Xu | 1 | 64 | 8.22 |
| Getian Ye | 2 | 81 | 9.47 |
| Gunawan Herman | 3 | 48 | 4.00 |
| Bang Zhang | 4 | 111 | 12.40 |