Kernel-Based Object Tracking

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.

IEEE Trans. Pattern Anal. Machine Intell., 25, 564-577, 2003.
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