摘要
提出了一种基于多特征自适应融合的核跟踪框架.利用目标特征的子模型集合构造了目标的多特征描述,通过线性加权方法将目标的多个特征集成在核跟踪方法中.根据各个特征子模型与当前目标及背景的相似性,提出了一种基于Fisher可分性度量的权值自适应更新机制;同时为了克服模型更新过程中的漂移,基于子模型的可分性提出了一种选择性更新策略,实现了在变化场景下的鲁棒跟踪.基于本文所提多特征跟踪框架,利用目标的颜色特征与LBP(Local binary pattern)纹理特征具体实现了多特征自适应融合的核跟踪方法,实验验证了本文方法的有效性.
A scheme is proposed to integrate multiple cues with kernel tracking by adaptive fusion to improve the robustness of object tracking in the time-variant scenario. The tracked object is represented by a set of submodels of each cue, and then the multiple cues are combined by linear weighting to realize kernel-based tracking. According to the discriminability of each cue between target and background, measured by Fisher rule, an adaptive mechanism is presented to update the cue weight. Furthermore, a selective submodel update strategy is utilized to alleviate the model drift. In experiments, we employ color cue and local binary pattern （LBP） texture cue to implement the scheme, and the results demonstrate the effectiveness of the proposed method in several real sequences testing.
出处
《自动化学报》
EI
CSCD
北大核心
2008年第4期393-399,共7页
Acta Automatica Sinica
基金
国家自然科学基金重点项目(60634030)资助
关键词
视觉跟踪
多特征融合
选择性更新
核跟踪
Visual object tracking, multiple cues fusion, selective update, kernel tracking
