Articulated deformable structure approach to human motion segmentation and shape recovery from an image sequence. Issue 3 (28th February 2019)

• Record Type: Journal Article
• Authors: Zhang, Peter Boyi; Hung, Yeung Sam
• Is Part Of: IET computer vision. Volume 13:Issue 3(2019)
• Page Start: 267
• Page End: 276
• Publication Date: 2019-02-28
• Subjects: image segmentation -- pose estimation -- image sequences -- image motion analysis -- tree searching; dynamic human body -- image sequence -- human body motion -- articulations -- different body parts -- local deformations -- body part -- nonrigid SFM -- fitting rigid subsets -- individual rigid subsets -- articulated kinematic chains -- blend-shape method -- human motion segmentation -- articulated deformable structure approach -- three-dimensional shape recovery; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2018.5365
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : The aim of this study is to perform motion segmentation and three‐dimensional shape recovery of a dynamic human body from an image sequence. The authors note that human body motion generally consists of large articulations between different body parts and small local deformations within each body part. On the basis of this notion, they develop an integrated framework that combines articulated structure from motion and non‐rigid SFM to estimate human body motion and shape as an articulated deformable structure. Unlike existing approaches that apply a low‐rank subspace method for motion segmentation, they use a metric constraint for identifying rigid subsets, which is more robust and, therefore, allow a more relaxed error threshold to be set for fitting rigid subsets, catering for small deformations within individual rigid subsets. They provide an automated statistical procedure for setting the aforementioned error threshold. The rigid subsets are then linked into articulated kinematic chains by minimum spanning tree search in a graph of joint costs. Finally, the blend‐shape method is applied to model local deformations of each individual subset. Experimental results show that the proposed method provides better performance for human motion segmentation and shape recovery compared with existing methods.