An Online Learning-based Framework for Tracking
This work addresses the problem of robust tracking for applications relying on noisy measurements, but it appears incremental as it builds on existing frameworks with a specific improvement.
The paper tackles the tracking problem by introducing a new online learning-based framework to address sensitivity to model mismatches, and shows that their algorithm outperforms the Bayesian algorithm on simulated data with slight mismatches.
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.