A 3D Dynamic Model of Human Actions for Probabilistic Image Tracking | Zendy

Ignasi Rius | Zendy; Daniel B. Rowe | Zendy; Jordi Gonzàlez | Zendy; Xavier Roca | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

A 3D Dynamic Model of Human Actions for Probabilistic Image Tracking

Author(s) -

Ignasi Rius,

Daniel B. Rowe,

Jordi Gonzàlez,

Xavier Roca

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-26153-2

DOI - 10.1007/11492429_64

Subject(s) - probabilistic logic , computer science , particle filter , set (abstract data type) , artificial intelligence , object (grammar) , sequence (biology) , computer vision , tracking (education) , image (mathematics) , action (physics) , space (punctuation) , statistical model , data mining , pattern recognition (psychology) , kalman filter , psychology , pedagogy , biology , genetics , programming language , physics , operating system , quantum mechanics

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research