Name
Title | ||
|---|---|---|
Moving Object Detection Using Tensor-Based Low-Rank and Saliently Fused-Sparse Decomposition. |
Abstract | ||
|---|---|---|
In this paper, we propose a new low-rank and sparse representation model for moving object detection. The model preserves the natural space-time structure of video sequences by representing them as three-way tensors. Then, it operates the low-rank background and sparse foreground decomposition in the tensor framework. On the one hand, we use the tensor nuclear norm to exploit the spatio-temporal redundancy of background based on the circulant algebra. On the other, we use the new designed saliently fused-sparse regularizer (SFS) to adaptively constrain the foreground with spatio-temporal smoothness. To refine the existing foreground smooth regularizers, the SFS incorporates the local spatio-temporal geometric structure information into the tensor total variation by using the 3D locally adaptive regression kernel (3D-LARK). What is more, the SFS further uses the 3D-LARK to compute the space-time motion saliency of foreground, which is combined with the $l_{1}$ norm and improves the robustness of foreground extraction. Finally, we solve the proposed model with globally optimal guarantee. Extensive experiments on challenging well-known data sets demonstrate that our method significantly outperforms the state-of-the-art approaches and works effectively on a wide range of complex scenarios. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TIP.2016.2627803 | IEEE Trans. Image Processing |
Keywords | Field | DocType |
Tensile stress,TV,Object detection,Three-dimensional displays,Matrix decomposition,Kernel,Estimation | Kernel (linear algebra),Object detection,Computer vision,Pattern recognition,Tensor,Matrix decomposition,Sparse approximation,Robustness (computer science),Matrix norm,Redundancy (engineering),Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
26 | 2 | 1057-7149 |
Citations | PageRank | References |
17 | 0.60 | 44 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenrui Hu | 1 | 80 | 4.12 |
| Yehui Yang | 2 | 88 | 4.26 |
| Wensheng Zhang | 3 | 98 | 18.14 |
| Yuan Xie | 4 | 407 | 27.48 |