Statistical Shape Learning for 3D Tracking

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Title: Statistical Shape Learning for 3D Tracking
Author: Sandhu, Romeil ; Lankton, Shawn ; Dambreville, Samuel ; Tannenbaum, Allen R.
Abstract: In this note, we consider the use of 3D models for visual tracking in controlled active vision. The models are used for a joint 2D segmentation/3D pose estimation procedure in which we automatically couple the two processes under one energy functional. Further, employing principal component analysis from statistical learning, can train our tracker on a catalog of 3D shapes, giving a priori shape information. The segmentation itself is information-based. This allows us to track in uncertain adversarial environments. Our methodology is demonstrated on some real sequences which illustrate its robustness on challenging scenarios.
Description: ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. DOI: 10.1109/CDC.2009.5399677 Presented at the 48th IEEE Conference on Decision and Control, held jointly with the 28th Chinese Control Conference, Shanghai, P.R. China, December 16-18, 2009.
Type: Proceedings
URI: http://hdl.handle.net/1853/32151
ISSN: 0191-2216
ISBN: 978-1-4244-3871-6
Citation: Romeil Sandhu, Shawn Lankton, Samuel Dambreville, and Allen Tannenbaum, "Statistical Shape Learning for 3D Tracking," Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, 2009, 4637-4642.
Date: 2009-12
Contributor: Georgia Institute of Technology. School of Electrical and Computer Engineering
Publisher: Georgia Institute of Technology
Institute of Electrical and Electronics Engineers
Subject: Shape learning techniques
Shape analysis
3D models
Active vision
Robust control

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