Tracking and data association
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
How Does CONDENSATION Behave with a Finite Number of Samples?
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
3D action modeling and reconstruction for 2d human body tracking
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Posture constraints for bayesian human motion tracking
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
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In this paper we present a method suitable to be used for human tracking as a temporal prior in a particle filtering framework such as CONDENSATION [5]. This method is for predicting feasible human postures given a reduced set of previous postures and will drastically reduce the number of particles needed to track a generic high-articulated object. Given a sequence of preceding postures, this example-driven transition model probabilistically matches the most likely postures from a database of human actions. Each action of the database is defined within a PCA-like space called UaSpace suitable to perform the probabilistic match when searching for similar sequences. So different, but feasible postures of the database become the new predicted poses.