We address the problem of online de-noising a stream of input points. We assume that the clean data is embedded in a linear subspace. We present two online algorithms for tracking ...
This paper presents a robust object tracking method via a spatial bias appearance model learned dynamically in video. Motivated by the attention shifting among local regions of a ...
We derive a probabilistic framework for robust, real-time, visual tracking of previously unseen objects from a moving camera. The tracking problem is handled using a bag-of-pixels ...
We present a novel predictive statistical framework to improve the performance of an Eigen Tracker which uses fast and efficient eigen space updates to learn new views of the obje...
Online boosting is one of the most successful online learning algorithms in computer vision. While many challenging online learning problems are inherently multi-class, online boo...
Amir Saffari, Martin Godec, Thomas Pock, Christian...