Data-Driven Importance Distributions for Articulated Tracking

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 highdimensional 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. Key words: Articulated tracking · Importance Distributions · Particle Filtering · Spatial Human Motion Models 1 Motivation Articulated tracking is the process of ...

• Type: Journal
• Year: 2011
• Where: EMMCVPR
• Authors: Søren Hauberg, Kim Steenstrup Pedersen
• Keywords: Background Subtraction, Computer Vision, EMMCVPR 2011, Linear Representation, Motion Models