Robust Object Tracking with Online Multiple Instance Learning

@article{Babenko2011RobustOT,
  title={Robust Object Tracking with Online Multiple Instance Learning},
  author={Boris Babenko and Ming-Hsuan Yang and Serge J. Belongie},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2011},
  volume={33},
  pages={1619-1632}
}
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… CONTINUE READING
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