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

TitleCompressed sensing for multi-view tracking and 3-D voxel reconstruction
Publication TypeConference Papers
Year of Publication2008
AuthorsReddy D, Sankaranarayanan AC, Cevher V, Chellappa R
Conference NameImage Processing, 2008. ICIP 2008. 15th IEEE International Conference on
Date Published2008/10//
Keywords3D, background-subtracted, coding;, Estimation, image, problems;multi-view, projections;silhouette, reconstruction;CS, reconstruction;video, sensing;multi-view, silhouettes;image, sparsity;sparse, theory;compressed, tracking;random, voxel

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.