Robust background subtraction method based on 3D model projections with likelihood
Hiroshi Sankoh, Akio Ishikawa, Sei Naito, Shigeyuki Sakazawa · 2010
We propose a robust background subtraction method for multi-view images, which is essential for realizing free viewpoint video where an accurate 3D model is required. Most of the conventional methods determine background using only visual information from a single camera image, and the precise silhouette cannot be obtained. Our method employs an approach of integrating multi-view images taken by multiple cameras, in which the background region is determined using a 3D model generated by multi-view images. We apply the likelihood of background to each pixel of camera images, and derive an integrated likelihood for each voxel in a 3D model. Then, the background region is determined based on the minimization of energy functions of the voxel likelihood. Furthermore, the proposed method also applies a robust refining process, where a foreground region obtained by a projection of a 3D model is improved according to geometric information as well as visual information. A 3D model is finally reconstructed using the improved foreground silhouettes. Experimental results show the effectiveness of the proposed method compared with conventional works.