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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.
Date : 2025-12-23 Size : 2.35mb User : Ali

提出了一种新的概率数据互联和粒子滤波相结合的新算法,并应用于杂波环境下的无源声纳系统中,该算法也可以很容易的应用于多目标情形。-There proposed a new method of data association called highest probability data association (HPDA) combined with particle filtering and applied to passive sonar tracking in clutter.The proposed HPDA algorithm can be easily extended to multi-target tracking problems.
Date : 2025-12-23 Size : 491kb User : ST
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