Real-Time object tracking algorithm employing on-line support vector machine and multiple candidate regeneration

A real-time object tracking algorithm is presented based on the on-line support vector machine (SVM) scheme. A new training framework is proposed, which enables us to select reliable training samples from the image sequence for tracking. Multiple candidate regeneration, a statistical method, is employed to decrease the computational cost, and a directional-edge-based feature representation algorithm is used to represent images robustly as well as compactly. The structure of the algorithm is designed especially for real-time performance, which can extend the advantages of SVM to most of the general tracking applications. The algorithm has been evaluated on challenging video sequences and showed robust tracking ability with accurate tracking results. The hardware implementation is also discussed, while verification has been done to prove the real-time ability of this algorithm.