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首页> 外文会议>2010 the 5th IEEE Conference on Industrial Electronics and Applications >A Kalman filtering based data fusion for object tracking
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A Kalman filtering based data fusion for object tracking

机译：基于卡尔曼滤波的数据融合用于目标跟踪

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摘要

To solve that single camera has its limitation of field of view, this paper proposed an object tracking method using multiple camera data fusion in image sequences. In this approach, a tracking filter and a multiple-view data fusion algorithm are applied. An estimation structure, called hierarchical estimation, is used to generate local and global estimate and to combine the estimates obtained from each camera views to form a global estimate. The advantage of this approach is the data of one camera view complements that of another camera view in order to obtain better target measurement information and to make more accurate estimates. A set of image sequences from multiple views are applied to evaluate performance. Computer simulation and experimental results indicate that this approach successfully tracks objects and has good estimation.
机译：为了解决单个摄像机的视场限制问题，提出了一种在图像序列中使用多摄像机数据融合的目标跟踪方法。在这种方法中，应用了跟踪滤波器和多视图数据融合算法。一种估计结构，称为分层估计，用于生成局部和全局估计，并组合从每个摄影机视图获得的估计以形成全局估计。这种方法的优点是一个摄像机视图的数据补充了另一个摄像机视图的数据，以便获得更好的目标测量信息并做出更准确的估计。应用来自多个视图的一组图像序列来评估性能。计算机仿真和实验结果表明，该方法成功跟踪了物体并具有良好的估计效果。

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