Unscented Kalman Filtering for Articulated Human Tracking

Autor:	Kim Steenstrup Pedersen, Anders Boesen Lindbo Larsen, Søren Hauberg
Rok vydání:	2011
Předmět:	business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Tracking system Kalman filter Tracking (particle physics) Motion capture Extended Kalman filter Filter (video) Computer vision Artificial intelligence business Particle filter Stereo camera
Zdroj:	Image Analysis ISBN: 9783642212260 SCIA
DOI:	10.1007/978-3-642-21227-7_22
Popis:	We present an articulated tracking system working with data from a single narrow baseline stereo camera. The use of stereo data allows for some depth disambiguation, a common issue in articulated tracking, which in turn yields likelihoods that are practically unimodal. While current state-of-the-art trackers utilize particle filters, our unimodal likelihood model allows us to use an unscented Kalman filter. This robust and efficient filter allows us to improve the quality of the tracker while using substantially fewer likelihood evaluations. The system is compared to one based on a particle filter with superior results. Tracking quality is measured by comparing with ground truth data from a marker-based motion capture system.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4e36e964ef6b33d8da72b662703f6ac1 https://doi.org/10.1007/978-3-642-21227-7_22 Zobrazit plný text záznamu