Target tracking algorithm based on Mean Shift and histogram ratio background weighted
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摘要: 针对Mean Shift跟踪算法目标模型中背景像素所造成的目标跟踪定位的偏差，提出了一种基于直方图比的背景加权的目标表示方法。该方法使用由目标核直方图和背景直方图的对数似然比值推导出的隶属度因子作为权值，通过只对目标模型进行加权变换，而不变换目标候选模型的方法，增强了目标和背景的可分性，有效减少了跟踪过程中背景像素的干扰，提高了目标定位的准确性。 仿真实验结果表明算法的有效性。Abstract: To resolve the problem that the background pixels in an object model induce localization errors in target tracking, a new target model establishing method based on HRBW is put forward. The fuzzy membership degree based on target/background histogram log-likelihood ratio was introduced in the kernel histogram for reducing the localization errors in target tracking produced by background pixels. The method transforms only the target model but not the target candidate model and decreases the probability of target model features that are prominent in the background. The results in experiments prove that the proposed algorithm not only accelerated the convergence, but also enhanced anti-interference ability.
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