基于改进的空间直方图相似性度量的粒子滤波视觉跟踪（英文）

Vision tracking based on improved similarity measurement of spatiogram and particle filter

空间直方图是直方图的一种推广,其增加了对更高阶空间信息的描述,提高了对目标的鉴别能力,而选择合适的空间直方图之间的相似性度量方法尤其重要。提出了一种基于目标背景区域相关度CTLB和JSD距离的空间直方图相似性度量,CTLB衡量了空间直方图每一个量化区间贡献值,减少了背景信息的干扰。将每一个量化区间看作为归属像素组成的高斯分布并应用JSD方法计算空间直方图的空间相似度,提高了度量方法的鉴别能力。最后,将该度量方法应用到了粒子滤波跟踪器中并和现有算法进行了比较。实验中两组测试序列跟踪误差均值分别是3.47像素和4.36像素,证明所提出的相似度度量方法优于现有方法,且跟踪结果更稳定、更鲁棒。

Spatiogram is a generalization of the histogram, which can more accurately describe the image as it can capture higher-order spatial moments. In view that selecting a suitable similarity measurement is critical for spatiogram based object tracking, an effective spatial histogram similarity measurement is proposed based on the correlation degree between the target/local background （CTLB） and Jensen-Shannon divergence （JSD）. The CTLB coefficient is calculated to weight each bin＇s contribution of spatiogram in complex scene, which can reduce the disturbance of background information. The spatial similarity is computed with JSD, regarding the spatial distribution of pixels corresponding to each bin as a Gaussian distribution. Finally, the proposed measurement method is compared with existing methods in the framework of Particle Filter. The mean tracking errors in the two testing sequences are 3.47 pixels and 4.36 pixels respectively, which indicate that the proposed method gives superior discriminative ability and performs more accurately and robustly in the tracking process.