Sciweavers

Share
ICIP
2008
IEEE

Compressed sensing for multi-view tracking and 3-D voxel reconstruction

8 years 8 months ago
Compressed sensing for multi-view tracking and 3-D voxel reconstruction
Compressed sensing(CS) suggests that a signal, sparse in some basis, can be recovered from a small number of random projections. In this paper, we apply the CS theory on sparse background-subtracted silhouettes and show the usefulness of such an approach in various multi-view estimation problems. The sparsity of the silhouette images corresponds to sparsity of object parameters (location, volume etc.) in the scene. We use random projections (compressed measurements) of the silhouette images for directly recovering object parameters in the scene coordinates. To keep the computational requirements of this recovery procedure reasonable, we tessellate the scene into a bunch of non-overlapping lines and perform estimation on each of these lines. Our method is scalable in the number of cameras and utilizes very few measurements for transmission among cameras. We illustrate the usefulness of our approach for multi-view tracking and 3-D voxel reconstruction problems.
Dikpal Reddy, Aswin C. Sankaranarayanan, Volkan Ce
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICIP
Authors Dikpal Reddy, Aswin C. Sankaranarayanan, Volkan Cevher, Rama Chellappa
Comments (0)
books