The real-time estimation of time-varying parameters from high-dimensional, heavy-tailed and corrupted data-streams is a common sub-routine in systems ranging from those for network monitoring and anomaly detection to those for traffic scheduling in data-centers.

Object tracking is a fundamental task in computer vision, requiring the localization of objects of interest across video frames.

In particular, the non-linear model is often optimized, whereas the reference KF model is not. We argue that both should be optimized similarly, and to that end present the Optimized KF ( OKF).

Both challenges involve modeling long-term dependencies.