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• W111330693 abstract "We present two data-driven importance distributions for particle filter-based 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." @default.
• W111330693 created "2016-06-24" @default.
• W111330693 creator A5061171736 @default.
• W111330693 creator A5072186051 @default.
• W111330693 date "2011-01-01" @default.
• W111330693 modified "2023-09-27" @default.
• W111330693 title "Data-Driven Importance Distributions for Articulated Tracking" @default.
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• W111330693 doi "https://doi.org/10.1007/978-3-642-23094-3_21" @default.
• W111330693 hasPublicationYear "2011" @default.
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