An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the Support of a High-Dimensional Distribution
Neural Computation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust tracking with discriminative ranking lists
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Visual object tracking via one-class SVM
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
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In this paper, visual tracking is treated as an object/back-ground classification problem. Multi-scale image patches are sampled to represent object and local background. A pair of binary and one-class support vector classifiers (SVC) are trained in every scale to model the object and background discriminatively and descriptively. Then a cascade structure is designed to combine SVCs in all scales. Incremental and decremental learning schemes for updating SVCs are used to adapt the environment variation, as well as to keep away from the classic problem of model drift. Two criteria are originally proposed to quantitatively evaluate the performance of tracking algorithms against model drift. Experimental results show superior accuracy and stability of our method to several state-of-the-art approaches.