Name
Abstract | ||
|---|---|---|
The unpreceded growth of intelligent surveillance systems has resulted in an urgent need for automatic analysis of the captured scenes. Automatic detection of an abnormal crowd in aerial images can provide useful information to prevent disasters. In fact, aerial images have the advantage of covering a very large view of the people distributed over a scene. Accordingly, we propose a high density crowd detection method in aerial images. In this method, we adapted the bag of words technique using the multiblock local binary pattern as a texture descriptor to extract low-level features. In addition, we also used a three-level classification strategy to reduce confusion between crowd density classes. The experimental results reveal the performance of our method while estimating the crowd density in a challenging context. (C) 2019 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1117/1.JEI.28.1.013047 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
bag of words,multiblock local binary pattern,aerial image,crowd density,crowd analysis,abnormal behavior | Computer vision,Pattern recognition,Computer science,Crowd density,Artificial intelligence | Journal |
Volume | Issue | ISSN |
28 | 1 | 1017-9909 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mliki Hazar | 1 | 11 | 4.91 |
| Olfa Arous | 2 | 0 | 0.34 |
| Mohamed Hammami | 3 | 181 | 30.54 |