Open AccessArticulated deformable structure approach to human motion segmentation and shape recovery from an image sequence
Author(s) -
Zhang Peter Boyi,
Hung Yeung Sam
Publication year - 2019
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5365
Subject(s) - artificial intelligence , computer vision , computer science , segmentation , structure from motion , motion estimation , kinematics , motion (physics) , motion capture , constraint (computer aided design) , subspace topology , metric (unit) , image segmentation , sequence (biology) , motion field , rigid body , mathematics , geometry , biology , genetics , operations management , physics , classical mechanics , economics
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.