Visual Tracking via Particle Filtering on the Affine Group

We propose a particle filtering-based visual tracker, in which the affine group is treated as the state. We first develop a general particle filtering algorithm that explicitly takes into account the geometry of the affine group. The tracking performance is further enhanced by the geometric auto-regressive process used for the state dynamics, combined state-covariance estimation, and robust measurement likelihood calculation using the incremental principal geodesic analysis of the image covariance descriptors. The feasibility of our proposed visual tracker is demonstrated via experimental studies.

• Type: Journal
• Year: 2010
• Where: IJRR
• Authors: Junghyun Kwon, Frank C. Park
• Keywords: Affine Group, Filtering-based Visual Tracker, IJRR 2010, Visual Tracker