In recent years, applications such as reproduction of traffic accident scene, calculation of traffic flow, etc. use UAVs to collect data by aerial images. If a UAV does not move, the coordinates of objects in images can be considered as reference for predicting parameters such as distance and direction. How-ever, this does not apply if the UAV is moving. In this research, methods of image processing are used to obtain the pinhole model which is similar to perpendicularly taken images, which should be distance-preserving. Then, the method of image stitching is used to find out the correlation of relative position between each image in order to construct a global coordinates system over every images. The common used methods of image stitching are not considered about distance-preserving, and cause the cumulative error usually. In this case, GPS information is used to estimate the starting position of images, then stitching those images to a panorama through applying SIFT feature pair and fine-tuning by the gradient method. At last, this paper integrates methods as mentioned into implementation and presents visualized data, then both the trajectory of UAV and the relative position of objects between images and the panorama can be observed by user in order to verify feasibility and precision of methods. Also the results of this paper can be applied with traffic flows detection in the future.