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Description: 详细的介绍了一种经典的跟踪算法，并给出了其他的跟踪算法-detailed account of a classical tracking algorithm, and gives the other tracking algorithm
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Size: 2784180 |
Author: 李治国 |
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Description: 详细的介绍了一种经典的跟踪算法，并给出了其他的跟踪算法-detailed account of a classical tracking algorithm, and gives the other tracking algorithm
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Size: 2784256 |
Author: 李治国 |
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Description: mean shift 算法用于目标跟踪,其中有4个文件.-mean shift algorithm for target tracking, including four documents.
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Size: 3072 |
Author: 李超 |
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Description: 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.-A new approach toward target representation and localization, the central component in visual trackingof non-rigid objects, is proposed. The feature histogram based target representations are regularizedby spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functionssuitable for gradient-based optimization, hence, the target localization problem can be formulated usingthe basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyyacoefficient as similarity measure, and use the mean shift procedure to perform the optimization. In thepresented 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 alsodiscussed. We describe only few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking .
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Size: 2779136 |
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