Dorin Comaniciu, Visvanathan Ramesh and Peter Meer(*)
Imaging and Visualization Department
Siemens Corporate Research
Princeton, NJ 08540
(*)Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08855, USA
A new approach toward target representation and localization, the
central component in visual tracking of non-rigid objects, is
proposed. The feature histogram based target representations are
regularized by spatial masking with an isotropic kernel. The masking
induces spatially-smooth similarity functions suitable for
gradient-based optimization, hence, the target localization problem
can be formulated using the basin of attraction of the local maxima.
We employ a metric derived from the Bhattacharyya coefficient as
similarity measure, and use the mean shift procedure to perform the
optimization. In the presented tracking examples the new method
successfully coped with camera motion, partial occlusions, clutter,
and target scale variations. Integration with motion filters and data
association techniques is also discussed. We describe only few of the
potential applications: exploitation of background information, Kalman
tracking using motion models, and face tracking.
