中文摘要：

目的当前大多数基于Mean-shift的跟踪算法都忽视了目标中密集的特征信息，本文有效利用密集特征信息，来提高跟踪的准确性。方法在目标模型中，常存在一些颜色特征相对聚集，形成一定大小的特征密集区，这些区域的面积或大或小，对人眼视觉跟踪异常重要。这些区域形成的空间结构信息，可以被利用到目标跟踪。提出一种高效的目标模型，通过计算密集特征区域面积，以及密集区质心到目标中心的距离，构建加权系数，通过该系数，来增加目标中分布相对集中的特征的权值，同时削弱离散特征的权值。同时使用零阶矩和目标模型与候选模型之间的相似度系数，估算目标的面积；再使用预测目标面积补偿法，对目标中因使用背景加权法而权重被削弱的特征区域，进行面积补偿；最后使用估算的目标区域面积以及二阶中心距，估算目标尺度和方向的改变。在跟踪过程中，背景如发生较大变化，则对目标模型进行更新。结果本文算法具有很好的尺度适应性，跟踪平均准确率在94．6％以上，得到较当前一些先进方法更好的准确度和效率。结论提出的算法能增加目标模型中不同特征权值间的差异，使得构建的目标模型具有较强区分目标和背景的能力，提高了定位目标的准确性；面积补偿法解决了目标因特征权重被削弱，而导致估算的目标面积小于实际面积的问题。

英文摘要：

Objective Most mean-shift based object tracking algorithms neglected information on the spatial distribution of dense features. This study uses dense features to enhance the reliability of tracking. Method Some color features gather on tracking objects, and each feature forms a region of certain size. These dense feature regions play an important role in hu- man vision. Information regarding the spatial structures of these dense feature regions can be used in object tracking. An effective and efficient tracking object model is presented. Intensive features are found, and the areas and distances be- tween the dense region centroids and the target object center are calculated to obtain the weight of each feature, which is applied to describe the tracked object. The intensive features in the target model are heavier than the discrete features. Simultaneously, the zero-order moment and the similarity coefficient between the target model and candidate models are used to estimate the target area. Subsequently, an area compensation method is used to compensate the object areas that are weakened by background weighting. Finally, the estimated area and the second-order center moment are used to adaptively estimate the object scale and direction. The object models are updated when the background shows significant changes. Result Experimental results show that the proposed method can adapt well to the object scale changes, with an average tracking accuracy of 〉 94.6% . In addition, the proposed method has higher accuracy, efficiency, and robustness than some state-of-the-art methods. Conclusion The proposed method increases the weight difference between different features in the target model. This method is efficient indistinguishing the target from the background. The area compensation method solves the problem wherein the estimated target area is less than the actual area because of the weak-ened target feature weight.