Abstract
A method for recovering a part-based description of human pose from single images of people is described. It is able to perform estimation efficiently in the presence significant background clutter, large foreground variation, self-occlusion and occlusion by other objects. This is achieved through two key developments. Firstly, a new formulation is proposed that allows partial configurations, hypotheses with differing numbers of parts, to be made and compared. This permits efficient global sampling in the presence of self and other object occlusions without prior knowledge of body part visibility. Secondly, a highly discriminatory likelihood model is proposed comprising two complementary components. A boundary component improves upon previous appearance distribution divergence methods by incorporating high-level shape and appearance information and hence better discriminates textured, overlapping body parts. An inter-part component uses appearance similarity of body parts to reduce the number of false-positive, multi-part hypotheses, hence increasing estimation efficiency. Results are presented for challenging images with unknown subject and large variations in subject appearance, scale and pose.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.