Abstract: Feature point tracking is an important technology to implement the visual odometry for navigation. Aiming at the problem of feature tracking with big errors caused by large motion of the video fixed on UAV, a multi-constraint KLT tracking strategy based on time-reversibility and bi-directional displacement constraint was proposed and the pyramid model was used for the hierarchical displacement computation of the tracking points. The new objective function was set up according to the fusion of forward and backward tracking. A new bi-directional displacement was constructed based on the displacements of forward and backward tracking, and the optimal estimation of the displacements was implemented in the structure of pyramid model. The experiment results demonstrate that the proposed algorithm improves the performance of precise tracking effectively and outperforms the similar tracker.