基于圆形采样和稀疏表示模型的鲁棒目标跟踪

为解决基于稀疏表示的跟踪算法在小样本空间中出现模板漂移而在大样本空间中实时性差的问题，提出了一种基于圆形采样的双重稀疏表示目标跟踪算法.该算法对跟踪矩形窗数据进行圆形采样，这不仅保证了目标的灰度和结构信息，而且减少了背景信息干扰.同时对稀疏表示得到的小模板系数引入距离权重判断函数，判断目标样本变化情况，提高模板更新效率.最后引入HOG（histogram of oriented gradient）特征，对稀疏表示得到的多个次优解进行二次稀疏表示，有效解决小样本数量少带来的估计误差.实验结果表明，该算法能够提高小样本空间中目标跟踪的鲁棒性和实时性. 

Robust Object Tracking Based on Circle Sampling and Sparse Representation

In order to deal with the drawbacks of template drifting in small sample space and the bad real-time performance in large sample space with the tracking algorithm based on sparse representation model, a tracking approach based on double sparse representation and circle shape sampling was proposed. The image data obtained from tracking rectangular frame were sampled with circle shape sampling model, not only preserving grayscale and structure information of tracking object, but also cutting down the disturbance from background pixels. Meanwhile, the trivial template coefficients gotten from sparse representation were analyzed with a distance weighting function to be used for obtaining target sample changing condition and improving efficiency of template updating. Finally, HOG(histogram of oriented gradient)feature was introduced for once more sparse representation to the second-best sparse solutions, which can cut down estimation error in small sample space. Experimental results show that the proposed algorithm can improve the robustness and efficiency of object tracking in small sample space.