Handling concept drifts in incremental learning with support vector machines
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Learning with Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-line ensemble SVM for robust object tracking
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Robust Object Tracking with Online Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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.