Abstract: Crowd motion estimation is an important part of crowd action analysis. Crowd motion Analysis in special places is a necessary action for maintaining the safety and social stability in public place and there is a research difficulty in the field of intelligent video monitoring. Existing approaches for crowd motion estimation based on traditional cameras have the limitation of small field-of-view and more blind spots. This paper proposes a crowd motion estimation approach based on the feature point optical flow employing the advantages of large field-of-view and no blind spot of fisheye cameras. Firstly, the original images are preprocessed using the method of background difference based on Gaussian Mixture Model with area feedback, and the region of interest (ROI) is obtained by circle fitting. Secondly, a feature point extraction method based on non-uniform sampling of edge density is presented to describe the moving crowd for improving the real-time performance as the same time as ensuring the accuracy of describing the crowd. And then the optical flow field is calculated using the method by Lucas & Kanade. Finally, a perspective weight model of the fisheye camera is developed to weighting the compute the motion vector and the motion direction and speed of the crowd in fisheye camera images in order to solve the issues of the size differences of the crowd in long and short distances and the distortion of fisheye images in this paper. The experimental results show that the proposed approach is effective and feasible for estimating the motion speed and orientation of the crowd in dense crowd. In addition, the proposed method provides an important research basis for crowd behavior analysis.