Robust human silhouette extraction with Laplacian fitting

Abstract

Human silhouette extraction has been a primary step to estimate human poses or classify activities from videos. While the accuracy of human silhouettes has great impact on the follow-on human pose/gait estimation, it has been important to guarantee the highly-accurate extraction of human silhouettes. However, traditional methods such as motion segmentation can be fragile due to the complexity of real-world environment. In this paper, we propose an automated human silhouette extraction algorithm to attain this highly-demanded task. In our proposed scheme, the initial motion segmentation of foreground objects was roughly computed by Stauffer's background subtraction using Gaussian mixtures, and then refined by the proposed Laplacian fitting scheme. In our method, the candidate regions of human objects are taken as the initial input, their Laplacian matrices are constructed, and Eigen mattes are then obtained by minimizing on Laplacian matrices. RANSAC algorithm is then applied to fit the Eigen mattes iteratively with inliers of the initially estimated motion blob. Finally, the foreground human silhouettes are obtained from the optimized matte fitting. Experimental results on a number of test videos validated that the proposed Laplacian fitting scheme enhances the accuracy in automated human silhouette extraction, exhibiting a potential use of our Laplacian fitting algorithm in many silhouette-based applications such as human pose estimation.