目标跟踪一直是计算机视觉领域的重要研究课题，广泛应用于视频监控、交通监视、医学诊断等领域。文中提出了一种基于二叉树模型的目标跟踪算法，该方法通过二叉树分块，将图像的目标区域分割为若干大小不一的同类块，块内像素相近，可用一个值或向量统一表示; 块间像素差距较大，从而构成整个目标的特征描述模型。并从准确性和跟踪速度两个方面对 CT 算法、基于四叉树模型的算法 (QT 算法) 和提出的基于二叉树模型的算法 (BT 算法) 进行了比较，结果表明: 与基于四叉树模型的算法相比，基于二叉树模型的跟踪算法在准确性方面几乎不受影响的前提下，跟踪速度显著提升; 与以跟踪速度快闻名的判别式 CT 算法相比，在跟踪速度大致相当的前提下，跟踪准确性却更好。

Object tracking has always been an important research topic in the field of computer vision． It is widely used in video surveillance，traffic monitoring，medical diagnosis and other fields． An object tracking algorithm based on binary tree model was proposed． The method divides the target area of the image into several homogene-ous blocks of different sizes，following the rule of the binary tree partition． The pixels in the block are similar and can be represented by a single value or vector whereas the pixels in different blocks differ from each significantly，thus forming the feature description model of the whole object． The CT algorithm，the quadtree model based algo-rithm (QT algorithm) and the proposed binary tree model based algorithm (BT algorithm) were compared from the aspects of accuracy and tracking speed． The results show that compared with the quadtree-based algorithm，the BT-based tracking algorithm can improve the tracking speed significantly without reducing the tracking accuracy．And compared with the discriminant CT-based algorithm，which is known for its fast tracking speed，the BT-based tracking accuracy is even better under the premise that the tracking speed is roughly equal．