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3D Scene Sensing and Object Recording
Volume: 28 | Article ID: art00017
Human detection from still depth images
  DOI :  10.2352/ISSN.2470-1173.2016.21.3DIPM-046  Published OnlineFebruary 2016

Human detection from depth images is gaining substantial attention since depth information facilitates object extraction from the background. In this paper, we propose a human detection method where search for humans is performed over regions obtained from a pre-segmentation of the depth image. Our segmentation scheme is based on K-means clustering of location, depth values and surface normals of pixels. Once homogeneous regions are determined, the top portion of the boundary of each region in the segmentation map is extracted and matched with realistic head-shoulder template curves. We evaluate our method both on a publicly available dataset, and on our new human detection dataset, which is composed of 500 depth images of humans in diverse poses acquired in varying indoor environments.

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Gulsum Nurdan Can, Helin Dutagaci, "Human detection from still depth imagesin Proc. IS&T Int’l. Symp. on Electronic Imaging: 3D Image Processing, Measurement (3DIPM), and Applications,  2016,

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Copyright © Society for Imaging Science and Technology 2016
Electronic Imaging
Society for Imaging Science and Technology