We present a novel robust tracking scheme for moving objects. Firstly we build the relationship between feature points and the centre of an object in the first frame. The Lucas Kanada tracker is then used to track these feature points in subsequent frames. We use these feature points to calculate a set of values for the centre location of the object. Then we propose a statistically optimal method to identify the values with large tracking errors, excluding which the location of centre is computed. Furthermore, the location of the feature points with tracking error is corrected by using a sub-image matching method. Experimental results using eight real video data sets demonstrate that our proposed method successfully removes the impact of tracking error of a few points on the centre location. Moreover, our solution prevents the accumulation of tracking errors.%提出了一种新颖、鲁棒的运动目标跟踪方法。在首帧图像建立特征点和物体中心点的关系．然后在后续帧图像使用LucasKanada跟踪方法跟踪这些特征点。利用这些特征点来计算一个物体中心位置的点集，并提出一种统计最优方法来鉴别那些存在跟踪误差较大的特征点，之后在计算中心点过程中把这些特征点剔除掉，并使用子图像匹配方法矫正存在跟踪误差的特征点。测试了8个实验视频，结果证明该算法不仅消弱了跟踪误差对中心位置计算的影响，而且基本消除了跟踪误差积累的弊端。
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