A tracking algorithm based on ORB

In order to improve the speed of tracking based on keypoint, we proposed an ORB-based tracking algorithm. We used the Gaussian mixture model to estimate the background. The difference of frame and background was treated as the foreground. All the ORB keypoints composed the feature set of object, which were extracted from foreground. In the foreground of each frame, we extracted ORB feature to match with the feature set of object and selected the good keypoints. We employed the Hamming distance to evaluate the quality of match. Then, these keypoints were used to find the position and update the feature set. The experiment results show that our tracking algorithm has a higher speed compared with SIFT-based algorithm and SURF-based algorithm, and accurately find the position of object.