Multi-view foreground segmentation via fourth order tensor learning

In this paper, we present a novel fuse-before-detect algorithm for multi-view foreground segmentation via fourth order tensor learning. By using several camera views, most of the existing algorithms first detect the various object features for each view and then fuse the data together for foreground segmentation or tracking. However, this kind of single view foreground segmentation algorithm always suffers from various environmental problems, such as reflection and shadow induced by shiny objects, especially floor and wall. These segmentation errors reduce the accuracy of the multi-view tracking algorithms. In the proposed algorithm, we first fuse multi-view camera data to a fourthorder tensor through multiple parallelized planes projections. An incremental fourth-order tensor learning algorithm is then employed to perform foreground segmentation in the fused tensor data. By collecting all the information from different views, this approach could restrain the specific environmental effects in each view and give better segmentation results. Experimental results are reported to show the performance of the proposed method is better than the state-of-the-art methods in challenged environments.