针对航空序列图像中模型没有任何先验知识且图像中的景物在不断刷新的情况，首先将图像分割成独立的区域，利用改进的Canny算子提取分割区域所包含的边缘，同时提出了区域特征比较因子，在此基础上给出了有效的特征模型提取和追踪方法，并分析了如何通过特征模型在航空序列图像中的移动来估计无人飞行器的飞行轨迹。通过对实际拍摄到的航空序列图像进行分析，表明本文的方法是有效的，对由航空序列图像的分析来估计无人飞行器的飞行轨迹提供了良好的基础。

The analysis of aerial sequential image is of significant importance to estimating the flight path of the unmanned air vehicle. In condition that there is no proiri knowledge on the model and the scenes in the aerial sequential images change continuously, first the images are filtered because of a large amount of noise. Second, the image is segmented into regions by analysis of the gray histogram, then the edge elements covered by the segmented regions are extracted by the improved Canny operator. In order to extract the model effectively, the concept of region feature comparison factor is proposed. Furthermore, the approach based on this concept to extract the suitable model is presented. And, due to robustness and low computational complexity of the matching method based on the hausdorff distance, it is used to resolve the model tracking in each frame of sequential images. Simultaneously, the model is updated in the process of the tracking on account of the model degradation in the aerial sequential images. In addition, on the basis of the projection principle of the camera, it is analyzed how to estimate the flight path of the unmanned air vehicle via the motion of the model in the aerial sequential images. After an experiment using the real aerial sequential images, it is demonstrated that the model extraction and tracking approach is feasible. It provides the foundation for the estimation of flight path of the unmanned air vehicle by use of the aerial image sequence.