TY - GEN
T1 - Robust 3D visual tracking using particle filtering on the SE(3) group
AU - Choi, Changhyun
AU - Christensen, Henrik I.
PY - 2011
Y1 - 2011
N2 - In this paper, we present a 3D model-based object tracking approach using edge and keypoint features in a particle filtering framework. Edge points provide 1D information for pose estimation and it is natural to consider multiple hypotheses. Recently, particle filtering based approaches have been proposed to integrate multiple hypotheses and have shown good performance, but most of the work has made an assumption that an initial pose is given. To remove this assumption, we employ keypoint features for initialization of the filter. Given 2D-3D keypoint correspondences, we choose a set of minimum correspondences to calculate a set of possible pose hypotheses. Based on the inlier ratio of correspondences, the set of poses are drawn to initialize particles. For better performance, we employ an autoregressive state dynamics and apply it to a coordinate-invariant particle filter on the SE(3) group. Based on the number of effective particles calculated during tracking, the proposed system re-initializes particles when the tracked object goes out of sight or is occluded. The robustness and accuracy of our approach is demonstrated via comparative experiments.
AB - In this paper, we present a 3D model-based object tracking approach using edge and keypoint features in a particle filtering framework. Edge points provide 1D information for pose estimation and it is natural to consider multiple hypotheses. Recently, particle filtering based approaches have been proposed to integrate multiple hypotheses and have shown good performance, but most of the work has made an assumption that an initial pose is given. To remove this assumption, we employ keypoint features for initialization of the filter. Given 2D-3D keypoint correspondences, we choose a set of minimum correspondences to calculate a set of possible pose hypotheses. Based on the inlier ratio of correspondences, the set of poses are drawn to initialize particles. For better performance, we employ an autoregressive state dynamics and apply it to a coordinate-invariant particle filter on the SE(3) group. Based on the number of effective particles calculated during tracking, the proposed system re-initializes particles when the tracked object goes out of sight or is occluded. The robustness and accuracy of our approach is demonstrated via comparative experiments.
UR - http://www.scopus.com/inward/record.url?scp=84871694253&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871694253&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2011.5980245
DO - 10.1109/ICRA.2011.5980245
M3 - Conference contribution
AN - SCOPUS:84871694253
SN - 9781612843865
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 4384
EP - 4390
BT - 2011 IEEE International Conference on Robotics and Automation, ICRA 2011
T2 - 2011 IEEE International Conference on Robotics and Automation, ICRA 2011
Y2 - 9 May 2011 through 13 May 2011
ER -