Name
Abstract | ||
|---|---|---|
In this paper, a new background subtraction framework is proposed to deal with possible scenarios occurring in natural scenes. In this method, a combination of two feature descriptors, namely color information in HSV color format and global texture descriptor T, are introduced to effectively identify background points under varying conditions. Using these features, an adaptive background model is constructed to automatically adapt to scene changes. The proposed framework is evaluated on common change detection datasets, showing improved performance compared to three well-known methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/VCIP47243.2019.8966047 | 2019 IEEE Visual Communications and Image Processing (VCIP) |
Keywords | Field | DocType |
Object detection,feature extraction,image-patch classification | Background subtraction,Computer vision,HSL and HSV,Object detection,Texture Descriptor,Change detection,Computer science,Feature extraction,Artificial intelligence,Feature based | Conference |
ISSN | ISBN | Citations |
1018-8770 | 978-1-7281-3724-7 | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mosin Russell | 1 | 9 | 1.52 |
| Ju Jia Zou | 2 | 198 | 20.00 |
| Gu Fang | 3 | 162 | 16.95 |
| Weidong Cai | 4 | 938 | 86.65 |