Developing a robust object tracker is a challenging task due to factors such
as occlusion, motion blur, fast motion, illumination variations, rotation,
background clutter, low resolution and deformation across the frames. In the
literature, lots of good approaches based on sparse representation have already
been presented to tackle the above problems. However, most of the algorithms do
not focus on the learning of sparse representation. They only consider the
modeling of target appearance and therefore drift away from the target with the
imprecise training samples. By considering all the above factors in mind, we
have proposed a visual object tracking algorithm by integrating a
coarse-to-fine search strategy based on sparse representation and the weighted
multiple instance learning (WMIL) algorithm. Compared with the other trackers,
our approach has more information of the original signal with less complexity
due to the coarse-to-fine search method, and also has weights for important
samples. Thus, it can easily discriminate the background features from the
foreground. Furthermore, we have also selected the samples from the un-occluded
sub-regions to efficiently develop the strong classifier. As a consequence, a
stable and robust object tracker is achieved to tackle all the aforementioned
problems. Experimental results with quantitative as well as qualitative
analysis on challenging benchmark datasets show the accuracy and efficiency of
our method.

通过整合基于稀疏表示的粗粒度到细粒度搜索策略和加权多实例学习算法，我们提出了一种稳定且鲁棒的目标跟踪算法，用于解决遮挡、运动模糊、快速运动、光照变化、旋转、背景杂乱、低分辨率和帧间形变等问题，并通过在具有权重的重要样本上区分前景和背景特征，高效地开发了强分类器。实验结果表明，我们的方法在具有挑战性的基准数据集上进行了定量和定性分析，显示了其准确性和效率。