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.
Highlights
Segmentation of the human body regions is essential in several applications
Once initial human body regions are obtained from the depth image, we can first project them onto the color image and treat them as segmentation seeds
The skeletons found from the depth images were projected to the color image coordinates using the method of [23]
Summary
Segmentation of the human body regions is essential in several applications. For example, segmented human bodies can be synthesized with a scene of another environment for immersive virtual reality games and telepresence applications. These methods cannot accurately estimate the human body pose and the resultant segmentation can be unreliable Depth sensors, such as Microsoft Kinect, have been very successful in the gaming industry. In Reference [12], both color and depth images are used to obtain the initial body skeletons and human body segmentation with rough boundaries. The most closely related method to ours is an adaptive multi-cue fusion framework [13] Both color and depth images are used to obtain the foreground region with precise boundaries, but the conventional multi-cue fusion framework tends to correct only mislabeled pixels around the initial foreground mask, and it is not tailored to the human body segmentation problem.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
