A 3D Dynamic Model of Human Actions for Probabilistic Image Tracking
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 . 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.
KeywordsHuman Motion Human Posture Human Body Model Probabilistic Search Feasible Posture
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