NDLI: A Novel Human Detection Approach Based on Depth Map via Kinect

Content Provider	IEEE Xplore Digital Library
Author	Yujie Shen Zhonghua Hao Pengfei Wang Shiwei Ma Wanquan Liu
Copyright Year	2013
Description	Author affiliation: Dept. of Comput., Curtin Univ., Perth, WA, Australia (Wanquan Liu) \|\| Sch. of Mechatron. Eng. & Autom., Shanghai Univ., Shanghai, China (Yujie Shen; Zhonghua Hao; Pengfei Wang; Shiwei Ma)
Abstract	In this paper, a new method of human detection based on depth map from 3D sensor Kinect is proposed. First, the pixel filtering and context filtering are employed to roughly repair defects on the depth map due to information inaccuracy captured by Kinect. Second, a dataset consisting of depth maps with various indoor human poses is constructed as benchmark. Finally, by introducing Kirsch mask and three-value codes to Local Binary Pattern, a novel Local Ternary Direction Pattern (LTDP) feature descriptor is extracted and is used for human detection with SVM as classifier. The performance for the proposed approach is evaluated and compared with other five existing feature descriptors using the same SVM classifier. Experiment results manifest the effectiveness of the proposed approach.
Starting Page	535
Ending Page	541
File Size	590820
Page Count	7
File Format	PDF
ISBN	9780769549903
ISSN	21607516
DOI	10.1109/CVPRW.2013.85
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Context Integrated circuits Histograms Filtering Noise Feature extraction
Content Type	Text
Resource Type	Article

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