基于主分量寻踪的鲁棒视觉跟踪

传统子空间跟踪易受到模型漂移的影响而导致跟踪失败.针对此问题,本文提出一种基于主分量寻踪的鲁棒视觉跟踪方法.该方法以多个模板张成的子空间作为目标表观模型,利用主分量寻踪求解候选目标的误差分量,在粒子滤波框架下利用候选目标的误差分量估计最优状态参数.为了适应目标表观变化并克服模型漂移,本文提出一种模板更新方法.当跟踪结果与目标模板相似时,该方法利用跟踪结果更新目标模板,否则利用跟踪结果的低秩分量更新目标模板.在多个具有挑战性的图像序列上的实验结果表明:与现有跟踪方法相比,文中的跟踪方法具有较优的跟踪性能.

Robust Visual Tracking via Principal Component Pursuit

The traditional subspaces based visual trackers are prone to model drifting.To deal with this problem, we propose a robust visual tracking method based on principal component pursuit.The proposed method represents objects with subspaces spanned by multiple templates, and finds error components of target candidates via principal component pursuit.The optimal state parameters are estimated by the error components of object candidates in particle filter framework.To adapt to changes of object appearance and avoid model drifting, a template update method is proposed.The proposed method updates the template set using tracking result when the tracking result is very similar to the templates;otherwise, it updates the template library with low-rank component corresponding to the tracking result.The experimental results on several challenging sequences show that the proposed method has better performance than that of the state-of-the-art tracker.