Visual object tracking via one-class SVM

Authors:
Li Li;Zhenjun Han;Qixiang Ye;Jianbin Jiao
Affiliations:
Graduate University of Chinese Academy of Sciences, Beijing, China;Graduate University of Chinese Academy of Sciences, Beijing, China;Graduate University of Chinese Academy of Sciences, Beijing, China;Graduate University of Chinese Academy of Sciences, Beijing, China
Venue:
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Year:
2010

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Abstract

In this paper, we propose a new visual object tracking approach via one-class SVM (OC-SVM), inspired by the fact that OCSVM's support vectors can form a hyper-sphere, whose center can be regarded as a robust object estimation from samples. In the tracking approach, a set of tracking samples are constructed in a predefined searching window of a video frame. And then a threshold strategy is proposed to select examples from the tracking sample set. Selected examples are used to train an OC-SVM model which estimates a hyper-sphere encircling most of the examples. Finally, we locate the center of the hyper sphere as the tracked object in the current frame. Extensive experiments demonstrate the effectiveness and robustness of the proposed approach in complex background.