Bai Bendu, Li Ying, Fan Jiulun, Price Chris, Shen Qiang
Appl Opt. 2015 Apr 1;54(10):2897-907. doi: 10.1364/AO.54.002897.
In this paper, we propose a novel object tracking method that can work well in challenging scenarios such as appearance changes, motion blurs, and especially partial occlusions and noise. Our method applies bilateral two-dimensional principal component analysis (Bi-2DPCA) for efficient object modeling and real-time computation requirement. An incremental Bi-2DPCA learning algorithm is proposed for characterizing the appearance changes of newly tracked objects. Also, to account for noise and occlusions, a sparse structure is introduced into our Bi-2DPCA object representation model. With this sparse structure, the appearance of an object can be represented by a linear combination of basis images and an additional noise image. The noise image, which indicates the location of noise and occlusions, can be used to effectively eliminate the influence caused by noise and occlusions and lead to a robust tracker. Instead of the reconstruction error commonly used in eigen-based tracking methods, a more accurate method is adopted for the computation of observation likelihood. The method is based on the energy distribution of coefficient matrix projected by Bi-2DPCA. Experimental results on challenging image sequences demonstrate the effectiveness of the proposed tracking method.
在本文中，我们提出了一种新颖的目标跟踪方法，该方法在诸如外观变化、运动模糊等具有挑战性的场景中，尤其是在部分遮挡和噪声情况下，能够良好地工作。我们的方法应用双边二维主成分分析（Bi-2DPCA）来满足高效的目标建模和实时计算需求。提出了一种增量式Bi-2DPCA学习算法，用于刻画新跟踪目标的外观变化。此外，为了应对噪声和遮挡，我们在Bi-2DPCA目标表示模型中引入了稀疏结构。借助这种稀疏结构，目标的外观可以由基图像和一个额外的噪声图像的线性组合来表示。该噪声图像指示了噪声和遮挡的位置，可用于有效消除噪声和遮挡所造成的影响，从而得到一个鲁棒的跟踪器。与基于特征值的跟踪方法中常用的重建误差不同，我们采用了一种更精确的方法来计算观测似然度。该方法基于Bi-2DPCA投影系数矩阵的能量分布。在具有挑战性的图像序列上的实验结果证明了所提出跟踪方法的有效性。
