Tracking using explanation-based modeling

Date
Authors
Chaudhuri, Kamalika
Freund, Yoav
Hsu, Daniel
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Description
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, the problem with these solutions is that they are very sensitive to model mismatches. In this paper, motivated by online learning, we introduce a new framework -- an {\em explanatory} 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 vastly outperforms the Bayesian algorithm.
Keywords
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Citation
Collections