Name
Abstract | ||
|---|---|---|
A video tracking method based on spatial constrained coding (SCC) is proposed in this study. To characterise local image structure information, the dense scale-invariant feature transform (SIFT) descriptor is extracted for each pixel in the image. The proposed tracking method uses SCC model which adopts a new constrained strategy - weighted code, which is achieved by considering the sum of the weighted codes based on grey values of neighbouring pixels and distances between them. The proposed model is able to obtain robust code of corresponding pixels in the frames of complex scenes by taking spatial information into account, which enhances the stability of coding and makes the tracker more robust for object tracking. Twelve challenging sequences involving partial or full occlusion, large pose variation and drastic illumination change are chosen to test the proposed method. The experimental results show the proposed method performs excellent in comparison with other previously proposed trackers. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1049/iet-cvi.2014.0017 | IET Computer Vision |
Keywords | Field | DocType |
object tracking,transforms,video coding,scc,scc model,sift descriptor,constrained strategy weighted code,image structure information,scale-invariant feature transform,spatial constrained coding,video tracking method,scale invariant feature transform | Spatial analysis,Computer vision,BitTorrent tracker,Scale-invariant feature transform,Pattern recognition,Coding (social sciences),Video tracking,Artificial intelligence,Pixel,Feature transform,Image structure,Mathematics | Journal |
Volume | Issue | ISSN |
9 | 1 | 1751-9632 |
Citations | PageRank | References |
0 | 0.34 | 25 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaolin Tian | 1 | 34 | 7.00 |
| Licheng Jiao | 2 | 5698 | 475.84 |
| Fandi Zhao | 3 | 0 | 0.34 |
| Xiao-hua Zhang | 4 | 118 | 11.70 |