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Constraining human motion for efficient tracking with a particle filter

AuthorsRius, Ignasi; Fernández, Carles; Mozerov, Mikhail; Gonzàlez, Jordi
KeywordsMotion analysis and recognition
Particle filters
Pattern recognition::Computer vision
Computer vision
Issue Date2006
PublisherUniversidad Autónoma de Barcelona
Citation1st CVC Workshop on Computer Vision: Advances in Research & Development: pp. 1-6 (2006)
AbstractParticle filters are one of the most commonly used techniques for full-body human tracking. However, given the high-dimensionality of the involved models, the number of required particles make the problem computationally very expensive. To overcome this, we present an action specific model of human postures which eases the process by guiding the prediction step of the particle filter, so only feasible human postures are considered. Thus, this model-based tracking approach samples from a first order motion model only those postures which are accepted by our action-specific model. In this manner, particles are propagated to locations in the search space with most a posteriori information avoiding particle wastage. We show that this scheme improves the efficiency and accuracy of the overall tracking approach.
DescriptionCVC Workshop on Computer Vision: Advances in Research & Development (CVCRD), 2006, Bellaterra (Spain)
Appears in Collections:(IRII) Comunicaciones congresos
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