Compressed sensing for multi-view tracking and 3-D voxel reconstruction
Compressed sensing for multi-view tracking and 3-D voxel reconstruction
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DOI：
10.1109/icip.2008.4711731
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发表时间：
2008-12
期刊：
影响因子：
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通讯作者：
Dikpal Reddy;Aswin C. Sankaranarayanan;V. Cevher;R. Chellappa
中科院分区：
文献类型：
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作者：
Dikpal Reddy;Aswin C. Sankaranarayanan;V. Cevher;R. Chellappa
压缩感知（CS）表明，在某些基础上稀疏的信号可以从少量随机投影中恢复。在本文中，我们将 CS 理论应用于稀疏背景扣除轮廓，并展示了这种方法在各种多视图估计问题中的有用性。轮廓图像的稀疏性对应于场景中对象参数（位置、体积等）的稀疏性。我们使用轮廓图像的随机投影（压缩测量）来直接恢复场景坐标中的对象参数。为了保持此恢复过程的计算要求合理，我们将场景细分为一堆不重叠的线，并对每条线进行估计。我们的方法在摄像机数量上是可扩展的，并且利用很少的测量来在摄像机之间进行传输。我们说明了我们的方法对于多视图跟踪和 3-D 体素重建问题的有用性。
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.