A matching method based on valid invariant feature part

The target tracking method based on correlation in image sequence is always invalid because of the magnitude or shape distortion and occlusion. In this paper a robust and highly accuracy matching algorithm called Matching Based on Valid Invariant Feature Part (MBVF) is proposed, which combines the target image invariant feature description, matching of feature, and recognition of valid feature. The feature descriptor is formed from a vector containing the values of all the grad magnitude and orientation entries that belong to the divided parts of target area. The features is robust to image rotation, distortion, addition of noise, change in 3D viewpoint, and change in illumination. The first step of the algorithm is to build the invariant feature descriptor of the target area in the referenced image. At the second step, a coarse position of the target is calculated using the traditional forecast and correlation method. And the invariant feature descriptors of all the possible points of the tracked target in image to be tracked are built also. Next, by comparing the invariant feature of the referenced target and the tracked target the valid feature parts of the feature are recognized. At last, similitude function is calculated according the valid feature parts in both images, which give the final fine position of the target in the tracked image. Experiment results show that the MBVF can deal with the target tracking and positioning problems in image sequence process and stereo image analysis automatically and accurately.