CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Understanding and Using Context
Personal and Ubiquitous Computing
Contextual Priming for Object Detection
International Journal of Computer Vision
Combining Multiple Motion Estimates for Vehicle Tracking
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Location of Mobile Devices Using Networked Surfaces
UbiComp '02 Proceedings of the 4th international conference on Ubiquitous Computing
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Robust Real-Time Face Detection
International Journal of Computer Vision
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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The use of context can be a very relevant cue for computer vision-based systems, in order to eliminate a lot of ambiguity and uncertainty, otherwise inherent, in the human-computer interaction. Despite of the fact that its obvious importance is widely acknowledged, the great majority of the systems nowdays still lack of this capability. In this paper, we propose faces as a primary contextual information for person detection and present face tracking as a basic procedure for context-driven focus of attention applications. Our proposal was implemented in a combined system, by integrating a motion-based approach (Particle Filter) and a model-based approach (Ada-Boost).