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Erschienen in:
2005 | OriginalPaper | Buchkapitel
A 3D Dynamic Model of Human Actions for Probabilistic Image Tracking
verfasst von : Ignasi Rius, Daniel Rowe, Jordi Gonzàlez, Xavier Roca
Erschienen in: Pattern Recognition and Image Analysis
Verlag: Springer Berlin Heidelberg
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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.