为了快速灵活地实现对图像序列中的目标运动的跟踪与描述，首先基于几何活动轮廓模型，提出了一种目标跟踪与运动估计的耦合变分模型，该模型可在进行多个目标跟踪的同时，估计运动矢量场，并以此修正跟踪的结果；然后分别从耦合模型的两个方面，讨论了模型对序列图像处理的执行效率和精度，接着针对耦合框架中的目标跟踪环节，改进了几何活动轮廓模型的外力场，从而增强了模型的跟踪能力和收敛速度；最后针对运动估计问题，由于耦合框架基于几何活动轮廓模型，因此框架在跟踪过程中，天然地提供了图像水平集信息，并在此信息的基础上，提出了一套用于快速计算图像序列局部目标的运动矢量场的方法，其对混合有非刚性运动的目标也能有较好的逼近结果。多种类型图像的数值实验结果证明，整套框架是有效的和鲁棒的，而且与经典光流方法进行的对比实验表明，新算法可以快速准确地同时估计图像序列中局部运动目标的轮廓位置与运动参数，从而为后续图像分析与处理打下了良好的基础。

For the purpose of rapidity and flexibility，this paper proposed a coupled functional framework for target tracking and motion estimation based on geometric active contour and level-set method. Our model estimates the displacement of object while tracking an objects contour and uses this displacement estimation to constrain active contour evaluating. Anglicizing image sequences needs models to have high efficiency and precision, we solve this problem with two methods：(1) Improving tracking effect and range as well as the accelerated convergence speed by modifying the active contours external force; （2）Proposed a new method for effective local motion estimation based on level-set information which is acquired from a coupled functional model. Because our entire formulation is based on geometric active contour, and formulation provide level set information of object in image naturally, therefore we can make use of level set information to assist constructing rapid motion estimation method. The second method deals with both rigid and non-rigid motion. Experiments on image sequences of varietals types such as MRI and video demonstrate efficiency and robustness of the proposed coupled model. One can use this model to get multi-object contour and motion estimation at the same time quickly. This model also provides solid foundation for further analysis and processing.