Tracking of multi-state hand models
using particle filtering and
a hierarchy of multi-scale image features
Ivan Laptev and Tony Lindeberg
Technical report CVAP245, ISRN KTH NA/P--00/12--SE.
Department of Numerical Analysis and Computer Science,
KTH (Royal Institute of Technology),
S-100 44 Stockholm, Sweden, September 2000.
Shortened version in IEEE Workshop on Scale-Space and Morphology,
Vancouver, Canada, July 2001, M. Kerckhove (Ed.),
Volume 2106 of Springer Verlag Lecture Notes in Computer Science,
pages 63--74.
Extended version of the underlying theory in
International Journal of Computer Vision, vol. 52, number 2/3, pages 97--120, 2003.
Abstract
This paper explores the use of hierarchical object representations
in terms of multi-scale image features for simultaneous tracking
and recognition of objects. Specifically, we consider an application
to hand gesture analysis, where hand models are tracked over multiple
postures (states). We propose a scale-invariant dissimilarity measure
for comparing scale-space features. Based on it, we evaluate the
likelihood of hierarchical, parameterized models containing different
types of image features at multiple scales. The likelihood is
constructed in such a way, that its maximization over different
models and their parameters allows for both model selection and
parameter estimation. These ideas are integrated with the framework
of particle filtering, involving simultaneous tracking and
recognition, and where a coarse-to-fine evaluation strategy
improves computational efficiency.
Based on the proposed approach, an application DrawBoard is developed,
where the user controls a drawing device with a set of qualitative
hand states and quantitative hand motions.