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Proceedings Paper

Skin subspace color modeling for daytime and nighttime group activity recognition in confined operational spaces
Author(s): Amir Shirkhodaie; Azin Poshtyar; Alex Chan; Shuowen Hu
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Paper Abstract

In many military and homeland security persistent surveillance applications, accurate detection of different skin colors in varying observability and illumination conditions is a valuable capability for video analytics. One of those applications is In-Vehicle Group Activity (IVGA) recognition, in which significant changes in observability and illumination may occur during the course of a specific human group activity of interest. Most of the existing skin color detection algorithms, however, are unable to perform satisfactorily in confined operational spaces with partial observability and occultation, as well as under diverse and changing levels of illumination intensity, reflection, and diffraction. In this paper, we investigate the salient features of ten popular color spaces for skin subspace color modeling. More specifically, we examine the advantages and disadvantages of each of these color spaces, as well as the stability and suitability of their features in differentiating skin colors under various illumination conditions. The salient features of different color subspaces are methodically discussed and graphically presented. Furthermore, we present robust and adaptive algorithms for skin color detection based on this analysis. Through examples, we demonstrate the efficiency and effectiveness of these new color skin detection algorithms and discuss their applicability for skin detection in IVGA recognition applications.

Paper Details

Date Published: 17 May 2016
PDF: 12 pages
Proc. SPIE 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV, 984213 (17 May 2016); doi: 10.1117/12.2226026
Show Author Affiliations
Amir Shirkhodaie, Tennessee State Univ. (United States)
Azin Poshtyar, Tennessee State Univ. (United States)
Alex Chan, U.S. Army Research Lab. (United States)
Shuowen Hu, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 9842:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXV
Ivan Kadar, Editor(s)

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