TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Active Contours Level Set Based Still Human Body Segmentation from Depth Images For Video-based Activity Recognition |
| 영문제목(English Title) |
Active Contours Level Set Based Still Human Body Segmentation from Depth Images For Video-based Activity Recognition |
| 저자(Author) |
Muhammad Hameed Siddiqi
Adil Mehmood Khan
Seok-Won Lee
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| 원문수록처(Citation) |
VOL 07 NO. 11 PP. 2839 ~ 2852 (2013. 11) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Context-awareness is an essential part of ubiquitous computing, and over the past decade video based activity recognition (VAR) has emerged as an important component to identify user’s context for automatic service delivery in context-aware applications. The accuracy of VAR significantly depends on the performance of the employed human body segmentation algorithm. Previous human body segmentation algorithms often engage modeling of the human body that normally requires bulky amount of training data and cannot competently handle changes over time. Recently, active contours have emerged as a successful segmentation technique in still images. In this paper, an active contour model with the integration of Chan Vese (CV) energy and Bhattacharya distance functions are adapted for automatic human body segmentation using depth cameras for VAR. The proposed technique not only outperforms existing segmentation methods in normal scenarios but it is also more robust to noise. Moreover, it is unsupervised, i.e., no prior human body model is needed. The performance of the proposed segmentation technique is compared against conventional CV Active Contour (AC) model using a depth-camera and obtained much better performance over it.
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| 키워드(Keyword) |
Human body segmentation
active contour
Bhattacharyya distance
activity recognition
depth camera
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| 파일첨부 |
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