Recovery of upper body poses in static images based on joints detection

Authors:
Zhilan Hu;Guijin Wang;Xinggang Lin;Hong Yan
Affiliations:
Department of Electronic Engineering, Tsinghua University, 9-306, East Main Building, Beijing 100084, China and Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Ko ...;Department of Electronic Engineering, Tsinghua University, 9-306, East Main Building, Beijing 100084, China;Department of Electronic Engineering, Tsinghua University, 9-306, East Main Building, Beijing 100084, China;Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, China and School of Electrical and Information Engineering, University of Sydney, NSW 2006, Australia
Venue:
Pattern Recognition Letters
Year:
2009

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Abstract

Recovering human body poses from static images is challenging without prior knowledge of pose, appearance, background and clothing. In this paper, we propose a novel model-based upper poses recovery method via effective joints detection. In our research, three observables are firstly detected: face, skin, and torso. Then the joints are properly initialized according to the observables and some heuristic configuration constraints. Finally the sample-based Markov chain Monte Carlo (MCMC) method is employed to determine the final pose. The main contributions of this paper include a robust torso detector through maximizing a posterior estimation, effective joints initialization, and two continuous likelihood functions developed for effective pose inference. Experiments on 250 real world images show that our method can accurately recover upper body poses from images with a variety of individuals, poses, backgrounds and clothing.