Scale and rotation invariant feature-based object tracking via modified on-line boosting
Object tracking is a major technique in image processing and computer vision. In this paper, we propose a new robust feature-based tracking scheme by employing adaptive classifiers to match the detected keypoints in consecutive frames. The novelty of this paper is that the design of online boosting is combined with the invariance of local features so that the classifier-based descriptions are formed in association with the scale and rotation information. Furthermore, we introduce a sample weighting mechanism in the on-line classifier updating, for the subsequent tracking. Experimental results demonstrate the robustness and accuracy of our proposed technique.