Computer Science – Computer Vision and Pattern Recognition
Scientific paper
2009-05-15
IEEE Transactions on Circuits and Systems for Video Technology, 2010
Computer Science
Computer Vision and Pattern Recognition
12 pages
Scientific paper
In this work we generalize the plain MS trackers and attempt to overcome standard mean shift trackers' two limitations. It is well known that modeling and maintaining a representation of a target object is an important component of a successful visual tracker. However, little work has been done on building a robust template model for kernel-based MS tracking. In contrast to building a template from a single frame, we train a robust object representation model from a large amount of data. Tracking is viewed as a binary classification problem, and a discriminative classification rule is learned to distinguish between the object and background. We adopt a support vector machine (SVM) for training. The tracker is then implemented by maximizing the classification score. An iterative optimization scheme very similar to MS is derived for this purpose.
Kim Junae
Shen Chunhua
Wang Hanzi
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