Description: 数据关联算法在目标跟踪中的应用，用matlab语言实现的。-Data association algorithm for target tracking in the application, using matlab language achievable. Platform: |
Size: 3072 |
Author:文如泉 |
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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 . Platform: |
Size: 2779136 |
Author: |
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Description: 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. Platform: |
Size: 2459648 |
Author:Ali |
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Description: 数据关联是多目标跟踪的一项关键技术。JPDA是大家公认的多目标跟踪中性能较好的数据关联算法，它
认为量测和目标是一一对应的关联关系，但在许多实际情况中，量测和目标是多一多对应的关系。针对上述情况，该文提
出了广义概率数据关联算法(Generalized Probability Data Association，GPDA)。文中从理论上对这两种算法的性能进行了
详细分析，并利用Monte Carlo技术对其性能进行了仿真比较。-Data association is one of the key technologies in multi—target tracking．And JPDA is considered as the best da·
ta association method．JPDA considers the association of measurements with targets is simply one-to-one problem．But in many
practical cases，the association of measurements with targets will be multiple—to—multiple problem．For this case，a
Generalized
Probability Data Association(GPDA)algorithm is proposed in this paper．Furthermore，this paper analyzes the performance of
these two algorithms theoretically．And we give the comparative analysis of those performances by using Monte Carlo method． Platform: |
Size: 431104 |
Author:minnie |
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Description: 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. Platform: |
Size: 2439168 |
Author:Felix |
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Description: 仅供参考，采用数据关联算法实现单个匀速运动目标的点迹与航迹的关联；PDAF方法第三种实现；-For reference only, using a single data association algorithm uniform motion targets associated trace and track the point PDAF third method to achieve Platform: |
Size: 3072 |
Author:yang |
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Search - target tracking using data association