SIFT flow for large-displacement object tracking

Traditional tracking methods place an emphasis on how to cope with the variations in target appearance effectively. However, when the motion displacement of the target between image frames becomes larger, these methods may be unstable. This paper presents a novel (to our knowledge) visual object tracking method. In this method, we first introduce scale-invariant feature transform (SIFT) flow into the tracking problem and develop a real-time motion prediction method to capture large displacement between consecutive image frames. Then we use belief propagation (BP) to convert the problem of finding maximum a posteriori probability (MAP) to globally minimizing an energy function to get the best matching pairs of points for producing good candidate regions of the target. And last, the refined point trajectories are obtained according to the bidirectional flow field consistency estimation and covariance region descriptor matching, which can update model states efficiently so as to achieve enhanced robustness for visual tracking. Compared with the state-of-art tracking methods, the experimental results demonstrate that the proposed algorithm shows favorable performance when the object undergoes large motion displacement between image frames.