  We study the tracking problem, namely, estimating the hidden state of an
object over time, from unreliable and noisy measurements. The standard
framework for the tracking problem is the generative framework, which is the
basis of solutions such as the Bayesian algorithm and its approximation, the
particle filters. However, these solutions can be very sensitive to model
mismatches. In this paper, motivated by online learning, we introduce a new
framework for tracking. We provide an efficient tracking algorithm for this
framework. We provide experimental results comparing our algorithm to the
Bayesian algorithm on simulated data. Our experiments show that when there are
slight model mismatches, our algorithm outperforms the Bayesian algorithm.
