摘要
提出了一种基于鉴别性与稳定性的自适应融合目标跟踪算法。在跟踪中,鉴别性度量目标与背景的区分程度,稳定性衡量跟踪框中心与目标实际中心之间的偏移程度。首先,对鉴别性与稳定性分开考虑,分别建模;而后将其引入自适应融合框架中,由此得到目标函数;最后优化目标函数得到自适应融合的权重。不同视频上的对比实验验证了该算法具有更高的跟踪准确性及稳定性。
A target tracking method based on adaptive fusion was proposed, in which the fusion algorithm is derived from tracking discrimination and stability. In target tracking, discrimination measured the difference between target and background, while stability measured the deviation degree from true target center to tracking result. Algorithmically, discrimination and stability were modeled separately first. Then, they were introduced into the adaptive fusion framework, and further to formulate an object function. Finally, this object function was optimized to obtain the adaptive fusion weights. Comparative experiments on different kinds of videos show that the proposed algorithm holds higher tracking precision and stability.
出处
《计算机应用》
CSCD
北大核心
2013年第A01期166-169,173,共5页
journal of Computer Applications
基金
国家自然科学基金面上项目(61075016)
中央高校基本科研业务费专项资金资助项目
关键词
自适应融合
目标跟踪
鉴别性
稳定性
adaptive fusion
target tracking
discrimination
stability
