Uncertainty in Artificial Intelligence
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An Online Learning-based Framework for Tracking
Kamalika Chaudhuri, Yoav Freund, Daniel Hsu
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.
Pages: 101-108
PS Link:
PDF Link: /papers/10/p101-chaudhuri.pdf
AUTHOR = "Kamalika Chaudhuri and Yoav Freund and Daniel Hsu",
TITLE = "An Online Learning-based Framework for Tracking",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "101--108"

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