Tracking Using CamShift Algorithm and Multiple Quantized Feature Spaces
Tracking Using CamShift Algorithm and Multiple Quantized Feature Spaces
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发表时间：
2004-06
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通讯作者：
J. G. Allen;R. Xu;Jesse S. Jin
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作者：
J. G. Allen;R. Xu;Jesse S. Jin
连续自适应均值漂移算法（CamShift）是均值漂移算法的一种适应，用于对象跟踪，旨在作为感知用户界面的头部和面部跟踪的一步。在本文中，我们回顾了CamShift算法，并扩展了一个默认的实现，以允许在任意数量和类型的特征空间中进行跟踪。为了计算像素值属于目标模型的新概率，我们在直方图回退之前用简单的单调递减的内核轮廓对多维直方图进行加权，投影。我们通过将结果与Mean Shift算法在等维量化特征空间中的通用实现进行比较来评估这种方法的有效性。本文的目的是检验这种方法的有效性。CamShift算法作为一种通用的目标跟踪方法，在没有对被跟踪目标做任何假设的情况下。
The Continuously Adaptive Mean Shift Algorithm (CamShift) is an adaptation of the Mean Shift algorithm for object tracking that is intended as a step towards head and face tracking for a perceptual user interface. In this paper, we review the CamShift Algorithm and extend a default implementation to allow tracking in an arbitrary number and type of feature spaces.In order to compute the new probability that a pixel value belongs to the target model, we weight the multidimensional histogram with a simple monotonically decreasing kernel profile prior to histogram back-projection.We evaluate the effectiveness of this approach by comparing the results with a generic implementation of the Mean Shift algorithm in a quantized feature space of equivalent dimension.The aim if this paper is to examine the effectiveness of the CamShift algorithm as a general-purpose object tracking approach in the case where no assumptions have been made about the target to be tracked.