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A. Elgammal, R. Duraiswami, D. Harwood and L. S. Davis, “Background and Foreground Modeling Using Nonparametric Kernel Density Estimation for Visual Surveillance,” Proceedings of the IEEE, Vol. 90, No. 7, 2002, pp. 1151-1163. doi:10.1109/JPROC.2002.801448

has been cited by the following article:

• TITLE: Simple Human Gesture Detection and Recognition Using a Feature Vector and a Real-Time Histogram Based Algorithm

  AUTHORS: Iván Gómez-Conde, David Olivieri, Xosé Antón Vila, Stella Orozco-Ochoa

  KEYWORDS: Computer Vision, Foreground Segmentation, Object Detection and Tracking, Gesture Recognition, Tele-Assistance, Telecare

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.2 No.4, November 16, 2011

  ABSTRACT: Gesture and action recognition for video surveillance is an active field of computer vision. Nowadays, there are several techniques that attempt to address this problem by 3D mapping with a high computational cost. This paper describes software algorithms that can detect the persons in the scene and analyze different actions and gestures in real time. The motivation of this paper is to create a system for thetele-assistance of elderly, which could be used as early warning monitor for anomalous events like falls or excessively long periods of inactivity. We use a method for foreg-round-background segmentation and create a feature vectorfor discriminating and tracking several people in the scene. Finally, a simple real-time histogram based algorithm is described for discriminating gestures and body positions through a K-Means clustering.