Kaimakis, P and Lasenby, J (2009) Physiological modelling for improved reliability in silhouette-driven gradient-based hand tracking. 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009. pp. 19-26.Full text not available from this repository.
We present a gradient-based motion capture system that robustly tracks a human hand, based on abstracted visual information - silhouettes. Despite the ambiguity in the visual data and despite the vulnerability of gradient-based methods in the face of such ambiguity, we minimise problems related to misfit by using a model of the hand's physiology, which is entirely non-visual, subject-invariant, and assumed to be known a priori. By modelling seven distinct aspects of the hand's physiology we derive prior densities which are incorporated into the tracking system within a Bayesian framework. We demonstrate how the posterior is formed, and how our formulation leads to the extraction of the maximum a posteriori estimate using a gradient-based search. Our results demonstrate an enormous improvement in tracking precision and reliability, while also achieving near real-time performance. © 2009 IEEE.
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Unnamed user with email firstname.lastname@example.org|
|Date Deposited:||15 Dec 2015 12:44|
|Last Modified:||29 Apr 2016 01:54|