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Real-time pose estimation of articulated objects using low-level motion

12 years 10 months ago
Real-time pose estimation of articulated objects using low-level motion
We present a method that is capable of tracking and estimating pose of articulated objects in real-time. This is achieved by using a bottom-up approach to detect instances of the object in each frame, these detections are then linked together using a high-level a priori motion model. Unlike other approaches that rely on appearance, our method is entirely dependent on motion; initial low-level part detection is based on how a region moves as opposed to its appearance. This work is best described as Pictorial Structures using motion. A sparse cloud of points extracted using a standard feature tracker are used as observational data, this data contains noise that is not Gaussian in nature but systematic due to tracking errors. Using a probabilistic framework we are able to overcome both corrupt and missing data whilst still inferring new poses from a generative model. Our approach requires no manual initialisation and we show results for a number of complex scenes and different classes of...
Ben Daubney, David P. Gibson, Neill W. Campbell
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2008
Where CVPR
Authors Ben Daubney, David P. Gibson, Neill W. Campbell
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