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

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The location of objects and people by the use of Global Positioning System (GPS) or Global System for Mobile Communications (GSM) network is increasingly used to provide location-based services. These technologies work well outdoors and when the required accuracy is not very high, up to 10 meters. This paper describes an algorithm for monitoring elderly people at home, by continuously taking its position, which uses the RSS information exchanged between Bluetooth devices, Weibull distributions and Bayesian classifiers. This algorithm has been validated in a real environment, an area of 13 × 12 meters, with several rooms and corridors, where zones of approximately 6 square meters have been delimited. Our algorithm achieved a rate of correct detections of 91.875%.