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Pattern Analysis and Applications

, Volume 15, Issue 4, pp 399–413 | Cite as

Human body segmentation based on deformable models and two-scale superpixel

  • Shifeng Li
  • Hu-Chuan LuEmail author
  • Xiang Ruan
  • Yen-Wei Chen
Theoretical Advances

Abstract

In this paper, we propose a novel method to segment human body in static images by graph cuts based on two deformable models at two-scale superpixel. In our study, body segmentation is decomposed into torso detection and lower body recovery. Based on the first-scale superpixel, the seeds of torso are obtained on the basis of the coarse torso region, which is estimated by an improved deformable torso model. For the lower body, we estimate the hip region to obtain the seeds of lower body at the second-scale superpixel. Besides, a deformable upper leg model is designed to derive more foreground seeds of the lower body. To avoid failure caused by the heavy dependence between the two hierarchies, a scheme of probabilistic hierarchical detection is presented. Experiments on our datasets containing 200 images photographed by ourselves and 100 other images collected from public datasets show that our approach can accurately segment human body in static images with a variety of poses, backgrounds and clothing. Segmenting the human body in static image based on deformable torso and upper leg models at two-scale superpixel.

Keywords

Body segmentation Superpixel Deformable model Graph cuts 

Notes

Acknowledgment

The work was supported by the Fundamental Research Funds for the Central Universities, No. DUT10JS05, the National Natural Science Foundation of China (NSFC), No. 61071209 and Omron Corporation's grant No. DUT09001.

Supplementary material

10044_2011_220_MOESM1_ESM.doc (853 kb)
Supplementary material 1 (DOC 853 kb)

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Shifeng Li
    • 1
  • Hu-Chuan Lu
    • 1
    Email author
  • Xiang Ruan
    • 2
  • Yen-Wei Chen
    • 3
  1. 1.Department of Electronic EngineeringDalian University of TechnologyDalianChina
  2. 2.Digtial Imaging and Mobile Devices Department Image Processing Design SectionOmronKyotoJapan
  3. 3.College of Information Science and Engineering, Ritsumeikan UniversityKusatsuJapan

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