Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Facial Action Coding Using Multiple Visual Cues and a Hierarchy of Particle Filters
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Particle filtering with factorized likelihoods for tracking facial features
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Particle Filter methods are one of the dominant tracking paradigms due to its ability to handle non-gaussian processes, multimodality and temporal consistency. Traditionally, the exponential growth on the number of particles required (and therefore in the computational cost) with respect to the increase of the state space dimensionality means one of the major drawbacks for these methods. The problem of part based tracking, central nowadays, is hardly tractable within this framework. Several efforts have been made in order to solve this problem, as the appearance of hierarchical models or the extension of graph theory by means of the Nonparametric Belief Propagation. Our approach relies instead on the use of Auxiliary Particle Filters, models the relations between parts dynamically (without training) and introduces a compatibility factor to efficiently reduce the growth of the computational cost. We did run the experiments presented without using a priori information.