Incorporating intensity distribution and spatial layout, this paper proposes a sequential Monte Carlo probabilistic tracking algorithm using intensity and spatial information.
结合图像的灰度分布和空间布局，提出了一种基于灰度和空间信息的序列蒙特卡罗概率跟踪算法。
The Nearest Near Joint Probabilistic Data Association(NNJPDA) is not used directly in multi-sensor multi-target tracking.
传统的最邻近联合概率数据关联算法（NNJPDA）不能直接用于多传感器对多目标的跟踪。
The probabilistic data association algorithm is applied in the spatial domain multi resolution frame and target tracking is implemented at the coarse resolution level.
这个算法在空间多分辨率框架下应用概率数据互联算法，在粗分辨率上实现模糊目标跟踪。
A time and space joint probabilistic data association algorithm is developed to solve the difficult problem of passive multisensor-multitarget tracking.
还提出了一种适合于实际工程应用的时空联合数据概率关摘要联算法，该算法解决了无源多传感器多目标跟踪的难题。
Joint Probabilistic Data Association (JPDA) algorithm can resolve the problem of tracking targets in clutter.
概率数据互联(JPDA)算法能很好地解决密集环境下的多目标跟踪问题。
Joint Probabilistic Data Association (JPDA) algorithm can resolve the problem of tracking targets in clutter.
概率数据互联(JPDA)算法能很好地解决密集环境下的多目标跟踪问题。
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