说明：  本文提出一种通过实时调整目标特征权值来进行背景自适应跟踪的算法。首先，定义了一种综合特征集合用以描述目标的颜色和局部轮廓。其次，提出了在滤波框架中对目标特征进行评估的算法，从而使得具有强区分能力的特征占有较大的权值，进而使其能够在跟踪过程起到较大的作用。采用传统的Kalman 滤波和粒子滤波对所提出的算法进行了验证。
(In this paper, we propose a new adaptive visual object tracking method based on online feature evaluation approach. First, a feature set is built by combining color histogram (HC) with gradient orientation histogram (HOG), which emphasizes both color and contour representation. Then a feature confidence evaluation approach is proposed to make features with higher confidences play more important roles in the instantaneous tracking ensuring that the tracking can adapt to the appearance change of both the object and its background. The feature evaluation approach is fused with filter frameworks, e.g. Kalman and Particle filter, to keep the temporal consistency of feature confidence evolution.)