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In this paper we present a vision system that performs tracking a human face in 3D. To achieve this we combine color and stereo cues to find likely image regions where face may exist. A greedy search algorithm examines for a face candidate focusing the action around the position of the face which was detected in the previous time step. The aim of the search(More)
This paper presents the application of a kernel particle filter for 3D body tracking in a video stream acquired from a single uncalibrated camera. Using intensity-based and color-based cues as well as an articulated 3D body model with shape represented by cylinders, a real-time body tracking in monocular cluttered image sequences has been realized. The(More)
This paper proposes the use of a particle filter with embedded particle swarm optimization as an efficient and effective way of dealing with 3d model-based human body tracking. A particle swarm optimization algorithm is utilized in the particle filter to shift the particles toward more promising configurations of the human model. The algorithm is shown to(More)
We present a system for fall detection in which the fall hypothesis , generated on the basis of accelerometric data, is validated by k-NN based classifier operating on depth features. We show that validation of the alarms in such a way leads to lower ratio of false alarms. We demonstrate the detection performance of the system using publicly available data.(More)
This paper proposes the use of a particle filter combined with color, depth information, gradient and shape features as an efficient and effective way of dealing with tracking of a head on the basis of image stream coming from a mobile stereovision camera. The head is modeled in the 2D image domain by an ellipse. A weighting function is used to include(More)