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R.: Compressed sensing for multi-view tracking and 3-D voxel reconstruction (2008)
| Content Provider | CiteSeerX |
|---|---|
| Author | Reddy, Dikpal Sankaranarayanan, Aswin C. Cevher, Volkan |
| Description | Compressed sensing suggests that a signal, sparse in some basis, can be recovered from a small number of random projections. In this paper, we apply this 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 separately. Our method is scalable in the number of cameras and utilizes very few measurements for transmission among cameras. We validate the performance of our approach for multi-view tracking and 3-D voxel reconstruction problems. Index Terms — Compressed Sensing, Tracking, 3-D Voxel Reconstruction 1. |
| File Format | |
| Language | English |
| Publisher Date | 2008-01-01 |
| Publisher Institution | In: Proc. IEEE Int’l Conference on Image Processing |
| Access Restriction | Open |
| Subject Keyword | Random Projection Compressed Measurement Recovery Procedure Small Number Index Term Non-overlapping Line Various Multi-view Estimation Problem Sparse Background Object Parameter Multi-view Tracking 3-d Voxel Reconstruction Computational Requirement 3-d Voxel Reconstruction Problem Silhouette Image |
| Content Type | Text |
| Resource Type | Article |
