Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

THIS WEBPAGE IS DEPRECATED - please visit our new website

L. Schwarz, D. Mateus, V. Castaneda, N. Navab
Manifold Learning for ToF-based Human Body Tracking and Activity Recognition
British Machine Vision Conference (BMVC), Aberystwyth, United Kingdom, August 2010 (bib)

In this paper, we propose a method for simultaneous human full-body pose tracking and activity recognition from time-of-flight (ToF?) camera images. Simple and sparse depth cues are used together with a prior motion model that constrains the tracking problem. Our model consists of low-dimensional manifolds of feasible poses for multiple activities. A particle filter allows us to efficiently evaluate various pose hypotheses over different activities and to select one that is most consistent with the observed depth image cues. We relate poses in the manifold embeddings to full-body poses and to observable depth cues using non-linear regression mappings. Our method is able to robustly detect changes of activity and adapt accordingly. We evaluate our method on a dataset containing 10 activities for 10 persons and show that we can track full-body pose and classify performed activities with a high precision which is discussed in the paper.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Edit | Attach | Refresh | Diffs | More | Revision r1.13 - 30 Jan 2019 - 15:16 - LeslieCasas

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif