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.