期刊
MULTIMEDIA TOOLS AND APPLICATIONS
卷 75, 期 6, 页码 3145-3160出版社
SPRINGER
DOI: 10.1007/s11042-014-2427-y
关键词
Target tracking; Mean shift; Foreground probability; Adaboost; Projection vector
According to the mean shift tracking algorithm, weights are used to reduce the background interference. However, weights also weaken the representation of the target at the same time. In order to reduce of weakness for model representation brought by the weight, weighted fusion which is composed of target model, candidate model and the probability that the pixel belonging to foreground is proposed to enhance the difference between foreground and background. The purpose is to resist the affection brought by background pixels. Firstly, weak classifiers composed of color and texture features are deduced by Bayesian and update the weak classifiers by changing the parameters of the Gauss distribution. Projection vector to distinguish the foreground and background is found through iteration. Then the projection vector obtained by foreground probability map and weight in mean shift is fused. The projection vector that strengthens the difference between foreground and background is updated to adapt to the changes of illumination or background. Finally, the target center position, scale and rotation angle are determined to achieve the target tracking by the moment features based on the improved weight.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据