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…

• NS
  National Science Foundation

  Awards: 0333451, CAREER, DGE-0333451, 0448615
• HR
  Honda Research Institute, USA
• UO
  University of California Merced
• OO
  Office of Naval Research

  Awards: N00014-08-1-0638, N00014