TY - GEN
T1 - A new framework for tracking by maintaining multiple global hypotheses for augmented reality
AU - Hayashi, Kenichi
AU - Kato, Hirokazu
AU - Nishida, Shogo
PY - 2007
Y1 - 2007
N2 - Several tracking techniques for augmented reality have been proposed. In feature point tracking, a pose is computed by minimizing the error between the observed 2D feature points and the back-projected feature points from the 3D scene model. This minimization problem is usually solved by nonlinear optimization. The main advantage of this approach is its accuracy. However, it is difficult to compute the correct pose unless an appropriate initial value is used. In addition, when an observation contains some errors, this approach does not guarantee a correct pose even if it converges to the global minimum. Therefore, once an incorrect pose is computed in a frame, either the tracking in the next frame may fail or the result will deviate from the correct pose. In this paper, we propose a new tracking framework for augmented reality. The proposed method tracks features as multiple local hypotheses based on not just one pose but multiple poses that are computed from pose estimation in the previous frame. Since multiple poses are maintained as global hypotheses, as long as the correct pose is contained in the hypotheses, tracking can be continued even in difficult situations such as a simple iterative scene with high-speed movements.
AB - Several tracking techniques for augmented reality have been proposed. In feature point tracking, a pose is computed by minimizing the error between the observed 2D feature points and the back-projected feature points from the 3D scene model. This minimization problem is usually solved by nonlinear optimization. The main advantage of this approach is its accuracy. However, it is difficult to compute the correct pose unless an appropriate initial value is used. In addition, when an observation contains some errors, this approach does not guarantee a correct pose even if it converges to the global minimum. Therefore, once an incorrect pose is computed in a frame, either the tracking in the next frame may fail or the result will deviate from the correct pose. In this paper, we propose a new tracking framework for augmented reality. The proposed method tracks features as multiple local hypotheses based on not just one pose but multiple poses that are computed from pose estimation in the previous frame. Since multiple poses are maintained as global hypotheses, as long as the correct pose is contained in the hypotheses, tracking can be continued even in difficult situations such as a simple iterative scene with high-speed movements.
UR - http://www.scopus.com/inward/record.url?scp=48349086109&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=48349086109&partnerID=8YFLogxK
U2 - 10.1109/ICAT.2007.9
DO - 10.1109/ICAT.2007.9
M3 - Conference contribution
AN - SCOPUS:48349086109
SN - 0769530567
SN - 9780769530567
T3 - Proceedings 17th International Conference on Artificial Reality and Telexistence, ICAT 2007
SP - 15
EP - 22
BT - Proceedings 17th International Conference on Artificial Reality and Telexistence, ICAT 2007
T2 - 17th International Conference on Artificial Reality and Telexistence, ICAT 2007
Y2 - 28 November 2007 through 30 November 2007
ER -