Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Skeletal Parameter Estimation from Optical Motion Capture Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Automatic learning of articulated skeletons from 3d marker trajectories
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Towards a bayesian approach to robust finding correspondences in multiple view geometry environments
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Part template: 3D representation for multiview human pose estimation
Pattern Recognition
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This paper presents a low cost real-time alternative to available commercial human motion capture systems. First, a set of distinguishable markers are placed on several human body landmarks and the scene is captured by a number of calibrated and synchronized cameras. In order to establish a physical relation among markers, a human body model (HBM) is defined. Markers are detected on all camera views and delivered as the input of an annealed particle filter scheme where every particle encodes an instance of the pose of the HBM to be estimated. Likelihood between particles and input data is performed through the generalized symmetric epipolar distance and kinematic constrains are enforced in the propagation step towards avoiding impossible poses. Tests over the HumanEva annotated dataset yield quantitative results showing the effectiveness of the proposed algorithm. Results over sequences involving fast and complex motions are also presented.