Collins Robert T, Liu Yanxi, Leordeanu Marius
Computer Science Engineering Department, The Pennsylvania State University, University Park, PA 16802, USA.
IEEE Trans Pattern Anal Mach Intell. 2005 Oct;27(10):1631-43. doi: 10.1109/TPAMI.2005.205.
This paper presents an online feature selection mechanism for evaluating multiple features while tracking and adjusting the set of features used to improve tracking performance. Our hypothesis is that the features that best discriminate between object and background are also best for tracking the object. Given a set of seed features, we compute log likelihood ratios of class conditional sample densities from object and background to form a new set of candidate features tailored to the local object/background discrimination task. The two-class variance ratio is used to rank these new features according to how well they separate sample distributions of object and background pixels. This feature evaluation mechanism is embedded in a mean-shift tracking system that adaptively selects the top-ranked discriminative features for tracking. Examples are presented that demonstrate how this method adapts to changing appearances of both tracked object and scene background. We note susceptibility of the variance ratio feature selection method to distraction by spatially correlated background clutter and develop an additional approach that seeks to minimize the likelihood of distraction.
本文提出了一种在线特征选择机制，用于在跟踪过程中评估多个特征，并调整所使用的特征集以提高跟踪性能。我们的假设是，最能区分目标与背景的特征也最适合跟踪目标。给定一组种子特征，我们计算目标和背景的类条件样本密度的对数似然比，以形成一组针对局部目标/背景区分任务量身定制的新候选特征。两类方差比用于根据新特征对目标和背景像素样本分布的分离程度对其进行排序。这种特征评估机制嵌入到一个均值漂移跟踪系统中，该系统会自适应地选择排名靠前的判别特征进行跟踪。文中给出的示例展示了该方法如何适应被跟踪目标和场景背景外观的变化。我们注意到方差比特征选择方法容易受到空间相关背景杂波干扰的影响，并开发了一种额外的方法来尽量减少干扰的可能性。
