Name
Abstract | ||
|---|---|---|
With the increasing amount of surveillance data, moving object segmentation in the compressed domain has drawn broad attention from both academy and industry. In this paper, we propose a novel moving object segmentation method towards H.264 compressed surveillance videos. First, the motion vectors (MV) are accumulated and filtered to achieve reliable motion information. Second, considering the spatial and temporal correlations among adjacent blocks, spatio-temporal Local Binary Pattern (LBP) features of MVs are extracted to obtain coarse and initial object regions. Finally, a coarse-to-fine segmentation algorithm of boundary modification is conducted based on the DCT coefficients. The experimental results validate that the proposed method not only can extract fairly accurate objects in compressed video, but also has a relatively low computational complexity. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/AVSS.2012.68 | AVSS |
Keywords | Field | DocType |
coarse-to-fine segmentation algorithm,object segmentation,spatio-temporal lbp,compressed domain,surveillance video,motion vector,reliable motion information,object segmentation method,surveillance data,initial object region,accurate object,computational complexity,feature extraction,data compression,image segmentation,vectors | Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Discrete cosine transform,Local binary patterns,Segmentation-based object categorization,Image segmentation,Feature extraction,Artificial intelligence,Data compression | Conference |
Citations | PageRank | References |
4 | 0.40 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianwei Yang | 1 | 4 | 0.40 |
| Shi-Zheng Wang | 2 | 77 | 8.39 |
| Zhen Lei | 3 | 3613 | 157.95 |
| Yanyun Zhao | 4 | 36 | 10.38 |
| Stan Z. Li | 5 | 8951 | 535.26 |