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Carnegie Mellon Robotics Institute

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Abstract

In this paper we demonstrate that the support vector tracking (SVT) framework ﬁrst proposed by Avidan is equivalent to the canonical Lucas-Kanade (LK) algorithm with a weighted Euclidean norm. From this equivalence we empirically demonstrate that in many circumstances the canonical SVT approach is unstable, and characterize these circumstances theoretically. We then propose a novel ?on-positive support kernel machine?(NSKM) to circumvent this limitation and allow the effective use of discriminative classiﬁcation within the weighted LK framework. This approach ensures that the pseudo-Hessian realized within the weighted LK algorithm is positive semideﬁnite which allows for fast convergence and accurate alignment/tracking. A further beneﬁt of our proposed method is that the NSKM solution results in a much sparser kernel machine than the canonical SVM leading to sizeable computational savings and much improved alignment performance.

Keywords

Support Vector Tracking

Notes

Text Reference

Simon Lucey, "Enforcing Non-Positive Weights for Stable Support Vector Tracking," IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June, 2008.

BibTeX Reference

@inproceedings{Lucey_2008_6029,
   author = "Simon Lucey",
   title = "Enforcing Non-Positive Weights for Stable Support Vector Tracking",
   booktitle = "IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)",
   month = "June",
   year = "2008",
}

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