一种新的空间直方图相似性度量方法及其在目标跟踪中的应用

摘要

在基于空间直方图的目标跟踪中，选择一种合适的度量两个空间直方图之间相似性的方法至关重要。该文提出一种新的空间直方图相似性度量方法。将空间直方图中的每个区间所对应像素的空间分布看作一个高斯分布，其均值和协方差矩阵为该区间内所有像素坐标的均值和协方差矩阵，然后用Jensen-Shannon Divergence(JSD)计算对应区间的空间分布相似度，而颜色特征的相似度采用具有强区分能力的直方图相交法来计算。理论和实验证明该文提出的相似性度量的稳定性好，区分能力强，其在静态图像上的整体性能优于已有度量方法，视频跟踪结果比已有方法更精确。%For spatiogram based object tracking, suitable similarity measure is critical. In this paper, a new spatiogram similarity measure is presented. The spatial distribution of the pixels corresponding to each bin is regarded as a Gaussian distribution, where the mean vector and covariance matrix are computed with all pixels belonging to the corresponding bin. Then, the similarity of two spatial distributions is computed with the Jensen-Shannon Divergence (JSD). The similarity of color feature is calculated by using histogram intersection, which is more discriminative than Bhattacharyya coefficient. Both theoretically and experimentally, the proposed measure is stable, and gives superior discriminative power than existing methods, and achieves promising performance in tracking object from single or sequence of images.