目的 由于当前大多数的追踪算法都是使用目标外观模型和特征进行目标的匹配，在长时间的目标追踪过程中，目标的尺度和形状均会发生变化，再加上计算机视觉误差，都会导致追踪的失误。提出一种高效的目标模型用于提高追踪的效率和成功率。方法 采用分割后提取的目标特征来进行建模表示外观结构，利用图像分割的方法，将被追踪的目标区域分割成多个超像素块，结合SIFT特征，形成词汇本，并计算每个词在词汇本中的权值，作为目标的外观模型。利用外观模型确定目标对象的关键点位置后，通过使用金字塔 Lucas-Kanade 追踪器预测关键点在下一帧图像中的位置，并移动追踪窗口位置。结果 结合点位移的加权计算有效地克服目标尺度和形状变化产生的问题。结论 实验结果表明在目标发生形变或光照变化的情况下，算法也能准确地、实时地追踪到目标。

Objective The appearance model and features of objects are commonly used for object matching.In long-term tracking, having large variations in scales, shape deformation, and other noises, it would be very challenging to success-fully keep tracking in this way.An effective object appearance model is proposed, which can improve the efficiency and effectiveness of object tracking.Method Image cues are used to describe the object appearance in this method.After image segmentation, the information is extracted from the superpixels (each segmentation block represents one superpixel).Then their SIFT descriptors are clustered to form a codebook.The weight of each word in the codebook is calculated to construct the target model to filter the superpixel points.Next the pyramidal Lucas-Kanade tracker is used to predict the location of the superpixel points in the next frame and move the tracking window.Result Combined with the weighting of point displacement, can conquer the variations in scales and shape deformation can be handled.Conclusion Experimental results show that the proposed method has good and robust performance even with appearance deformation and illumination changes.