Search - DATA ASSOCIATION AND TRACKING

Search - DATA ASSOCIATION AND TRACKING - List

[Special Effects] pdaf-demo DL : 0: Probabilistic Data Association Filter跟踪算法示例-Probabilistic Data Association Filter Tracking Algorithm example
Date : 2025-12-23 Size : 3kb User : liushan
[Special Effects] zhongzhilvbo4b3(4) DL : 0: 采用中值滤波法背景建模，图像处理，对连续图像进行目标跟踪和数据关联。-Median filtering method using background modeling, image processing, continuous image of the target tracking and data association.
Date : 2025-12-23 Size : 1kb User : 阿龙
[Special Effects] KernelTracking DL : 0: 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 .
Date : 2025-12-23 Size : 2.65mb User :