Robust Object Tracking with Online Multiple Instance Learning

In this paper we address the problem of tracking an object in a video given its location in the first frame and no other information. Recently, a class of tracking techniques called “tracking by detection” has 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 degrade the classifier and can cause 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 propose a novel online MIL algorithm for object tracking that achieves superior results with realtime performance. We...

• Type: Journal
• Year: 2011
• Where: PAMI
• Authors: Boris Babenko, Ming-Hsuan Yang, Serge Belongie
• Keywords: Classi, Computer Networks, Inaccuracies, PAMI 2011, Time Speeds