Data-driven importance distributions for articulated tracking

Søren Hauberg, Kim Steenstrup Pedersen

Abstract

We present two data-driven importance distributions for particle filterbased
articulated tracking; one based on background subtraction, another on depth
information. In order to keep the algorithms efficient, we represent human poses
in terms of spatial joint positions. To ensure constant bone lengths, the joint
positions are confined to a non-linear representation manifold embedded in a
high-dimensional Euclidean space. We define the importance distributions in the
embedding space and project them onto the representation manifold. The resulting
importance distributions are used in a particle filter, where they improve both
accuracy and efficiency of the tracker. In fact, they triple the effective number of
samples compared to the most commonly used importance distribution at little
extra computational cost.
Original languageEnglish
Title of host publicationEnergy Minimization Methods in Computer Vision and Pattern Recognition : 8th International Conference, EMMCVPR 2011, St. Petersburg, Russia, July 25-27, 2011. Proceedings
EditorsYuri Boykov, Fredrik Kahl, Victor Lempitsky, Frank R. Schmidt
Number of pages13
PublisherSpringer
Publication date2011
Pages287-299
ISBN (Print)978-3-642-23093-6
ISBN (Electronic)978-3-642-23094-3
DOIs
Publication statusPublished - 2011
Event8th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition - Sankt Petersborg, Russian Federation
Duration: 25 Jul 201127 Jul 2011
Conference number: 8

Conference

Conference8th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Number8
Country/TerritoryRussian Federation
CitySankt Petersborg
Period25/07/201127/07/2011
SeriesLecture notes in computer science
Volume6819
ISSN0302-9743

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