在视频处理领域的运动目标跟踪问题中，卡尔曼滤波器（KF）与扩展卡尔曼滤波器（EKF）已经得到了广泛的应用，但在复杂背景或是目标高机动运动的情况下跟踪效果并不理想。提出一种基于交互多模型算法（IMM），并采用去偏转换测量卡尔曼滤波器（CMKF-D）对运动目标进行跟踪的算法。该算法有效地解决了单一模型无法与运动特性相匹配的问题，并克服了KF、EKF对非线性模型线性化所引入的误差。以足球视频为例进行仿真实验，结果表明该算法有效地提高了视频序列中运动目标跟踪的准确率。

For tracking and measuring maneuvering target in sports video frequency ， Kalman Filter(KF) and Extended Kalman Filter (EKF) has been widely used,but with low accuracy. A model that is combined with Interaction Multiple Model (IMM) algorithm and Debiased consistent Converted Measurements Kalman Filter (CMKF-D) algorithm is proposed for tracking and measuring the target in sports video frequency . It avoids the error that may be caused by transferring non-linear model to linear model through EKF and KF. The football video frequency simulation shows this algorithm can promote the tracking performance of maneuvering target in sports video frequency.