Graph cut-based human body segmentation in color images using skeleton information from the depth sensor

Jonha Lee, Dong Wook Kim, Chee Sun Won, Seung Won Jung

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Segmentation of human bodies in images is useful for a variety of applications, including background substitution, human activity recognition, security, and video surveillance applications. However, human body segmentation has been a challenging problem, due to the complicated shape and motion of a non-rigid human body. Meanwhile, depth sensors with advanced pattern recognition algorithms provide human body skeletons in real time with reasonable accuracy. In this study, we propose an algorithm that projects the human body skeleton from a depth image to a color image, where the human body region is segmented in the color image by using the projected skeleton as a segmentation cue. Experimental results using the Kinect sensor demonstrate that the proposed method provides high quality segmentation results and outperforms the conventional methods.

Original languageEnglish
Article number393
JournalSensors
Volume19
Issue number2
DOIs
StatePublished - 2 Jan 2019

Keywords

  • Depth image
  • Graph cut
  • Human body segmentation
  • Image segmentation
  • Kinect sensor
  • Skeleton

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