摘要
为更好地应对跟踪过程中复杂的场景变化问题,提出一种采用多种特征融合进行跟踪的方法。算法在粒子滤波的框架下,通过在跟踪过程中对每一个特征进行不确定性度量,计算动态的特征权值,从而完成了自适应的特征融合。利用颜色、空间和纹理特征的互补特性,提升了算法的跟踪性能。实验结果表明,算法能够很好地适应目标尺度、旋转、运动模糊等复杂场景的变化。与近年来流行的算法相比,所提出的算法具有明显优势,能够很好地完成跟踪任务。
In order to deal with complex scene change problem in the tracking process, we propose a tracking algo- rithm via multiple feature fusion. Under the framework of particle filter, dynamic feature weights are calculated by making an uncertain measure of each feature in the tracking process, which results in adaptive feature fusion. The algorithm uses the complementarity of color, space and texture features to improve the tracking performance. Experimental results show that the algorithm can adapt to complex scene changes such as scale, rotation and motion blur Compared with traditional algorithms, the proposed algorithm has obvious advantages to complete the tracking task.
作者
侯志强
王利平
郭建新
褚鹏
Hou Zhiqiang;Wang Liping;Guo Jianxin;Chu Peng(School of Information Engineering, Xijing University, Xi'an, Shaanxi 710123, China)
出处
《光电工程》
CAS
CSCD
北大核心
2018年第5期36-43,共8页
Opto-Electronic Engineering
基金
国家自然科学基金资助项目(61473309)~~
关键词
视觉跟踪
特征融合
颜色
空间
纹理
visual tracking
feature fusion
color
space
texture