Visual tracking with online Multiple Instance Learning

• Published in: 2009 IEEE Conference on Computer Vision and Pattern Recognition pp. 983 - 990
• Main Authors: Babenko, Boris, Ming-Hsuan Yang, Belongie, Serge
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2009
• Subjects: Degradation; Robustness; Supervised learning
• ISBN: 1424439922; 9781424439928
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2009.5206737

In this paper, we address the problem of learning an adaptive appearance model for object tracking. In particular, a class of tracking techniques called "tracking by detection" have been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrades the classifier and can cause further drift. In this paper we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems, and can therefore lead to a more robust tracker with fewer parameter tweaks. We present a novel online MIL algorithm for object tracking that achieves superior results with real-time performance.