Anzeige
Erschienen in:
2012 | OriginalPaper | Buchkapitel
Real-Time Object Tracking Algorithm Employing On-Line Support Vector Machine and Multiple Candidate Regeneration
verfasst von : Pushe Zhao, Renyuan Zhang, Tadashi Shibata
Erschienen in: Artificial Intelligence and Soft Computing
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.