• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Active Contours Level Set Based Still Human Body Segmentation from Depth Images For Video-based Activity Recognition


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 users 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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Cite this article

[IEEE Style]
M. H. Siddiqi, A. M. Khan and S. Lee, "Active Contours Level Set Based Still Human Body Segmentation from Depth Images For Video-based Activity Recognition," KSII Transactions on Internet and Information Systems, vol. 7, no. 11, pp. 2839-2852, 2013. DOI: 10.3837/tiis.2013.11.017.

[ACM Style]
Muhammad Hameed Siddiqi, Adil Mehmood Khan, and Seok-Won Lee. 2013. Active Contours Level Set Based Still Human Body Segmentation from Depth Images For Video-based Activity Recognition. KSII Transactions on Internet and Information Systems, 7, 11, (2013), 2839-2852. DOI: 10.3837/tiis.2013.11.017.