Statistical and Variational Methods for Problems in Visual Control
Abstract
Following the position of moving objects based on the information delivered by single or multiple optical sensors (e.g., video cameras) is the objective of visual tracking. The need for visual tracking is ubiquitous and a multitude of approaches exist for the solution of this tracking problem. Increasingly, computer vision algorithms are required to provide additional information beyond a simple track point, and more complex methodologies are needed to produce the desired information. For noisy, cluttered, and/or dynamic scenes the ability to provide a smooth and faithful signal is essential which leads of course to the entire issue of filtering. In this research program, we have developed a novel visual tracking approach, using statistical variational methods. In particular, we have developed a geometric particle filter for controlled active vision. This has been applied to various tracking problems including tracking through turbulence and UAVs flying in formation.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 02, 2009
- Accession Number
- ADA531631
Entities
People
- Allen Tannenbaum
Organizations
- Georgia Tech