Robust Human Silhouette Extraction with Laplacian Fitting

Al-Maadeed, Somaya, Almotaeryi, Resheed, Jiang, Richard and Bouridane, Ahmed (2014) Robust Human Silhouette Extraction with Laplacian Fitting. Pattern Recognition Letters, 49. pp. 69-76. ISSN 0167-8655

Full text not available from this repository. (Request a copy)
Official URL:


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.

Item Type: Article
Additional Information: Published online 16-6-2014 ahead of print.
Uncontrolled Keywords: Human pose, silhouette, background subtraction, Laplacian matrix, RANSAC
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 10 Jul 2014 10:18
Last Modified: 13 Oct 2019 00:37

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics