Comaniciu, D.
Ramesh, V.
Meer, P.
Real-Time Vision & Modeling Dept., Siemens Corporate Res., Princeton, NJ, USA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: May 2003
Volume: 25,
Issue: 5
On page(s): 564- 577
ISSN: 0162-8828
INSPEC Accession Number: 7667773
Digital Object Identifier: 10.1109/TPAMI.2003.1195991
Posted online: 2003-04-29 13:07:47.0
Abstract
A new approach toward target representation and localization, the central component in visual tracking of nonrigid 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 a few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking.
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