A new framework for on-line object tracking based on SURF

We present a new object tracking scheme by employing adaptive classifiers to match the corresponding keypoints between consecutive frames. The detection of interest points is a critical step in obtaining robust local descriptions. This paper proposes an efficient feature detector based on SURF, by incrementally predicting the search space, to enhance the repeatability of the tracked interest points. Instead of computing the SURF descriptor, we construct a classifier-based descriptor using on-line boosting. With on-line learning ability based on our sample weighting mechanism, the classifier maintains its discriminative power to establish robust feature description and reliable points matching for subsequent tracking. In addition, matching candidates are validated using improved RANSAC to ensure correct updates and accurate tracking. All of these ingredients contribute measurably to improving overall tracking performance. Experimental results demonstrate the robustness and accuracy of our proposed technique.

► Object tracking is performed by matching keypoints using online classifiers. ► Compute the keypoints by incrementally predicting the object region. ► The scale and dominant orientation of the keypoint is fused in online boosting. ► Employ a non-uniform sampling strategy during RANSAC. ► Discriminative samples are selected and weighted in online learning.