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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Rius, I., Rowe, D., Gonzalez, J., & Roca, X. (2005). A 3D dynamic model of human actions for probabilistic image tracking. In Lecture Notes in Computer Science (Vol. 3522, pp. 529–536). Springer Verlag. https://doi.org/10.1007/11492429_64
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