Object trajectory tracking is an important topic in many different areas. It is widely used in robot technology, traffic, movie industry, and others. Optical flow is a useful method in the object tracking branch and it can calculate the motion of each pixel between two frames, and thus it provides a possible way to get the trajectory of objects. There are numerous papers describing the implementation of optical flow. Some results are acceptable, but in many projects, there are limitations. In most previous applications, because the camera is usually static, it is easy to apply optical flow to identify the moving targets in a scene and get their trajectories. When the camera moves, a global motion will be added to the local motion, which complicates the issue. In this thesis we use a combination of optical flow and image correlation to deal with this problem, and have good experimental results. For trajectory estimation, we incorporate a Kalman Filter with the optical flow. Not only can we smooth the motion history, but we can also estimate the motion into the next frame. The addition of a spatial-temporal filter improves the results in our later process.