TY - GEN
T1 - High accuracy optical flow serves 3-D pose tracking
T2 - 9th European Conference on Computer Vision, ECCV 2006
AU - Brox, Thomas
AU - Rosenhahn, Bodo
AU - Cremers, Daniel
AU - Seidel, Hans Peter
PY - 2006
Y1 - 2006
N2 - Tracking the 3-D pose of an object needs correspondences between 2-D features in the image and their 3-D counterparts in the object model. A large variety of such features has been suggested in the literature. All of them have drawbacks in one situation or the other since their extraction in the image and/or the matching is prone to errors. In this paper, we propose to use two complementary types of features for pose tracking, such that one type makes up for the shortcomings of the other. Aside from the object contour, which is matched to a free-form object surface, we suggest to employ the optic flow in order to compute additional point correspondences, Optic flow estimation is a mature research field with sophisticated algorithms available. Using here a high quality method ensures a reliable matching, In our experiments we demonstrate the performance of our method and in particular the improvements due to the optic flow.
AB - Tracking the 3-D pose of an object needs correspondences between 2-D features in the image and their 3-D counterparts in the object model. A large variety of such features has been suggested in the literature. All of them have drawbacks in one situation or the other since their extraction in the image and/or the matching is prone to errors. In this paper, we propose to use two complementary types of features for pose tracking, such that one type makes up for the shortcomings of the other. Aside from the object contour, which is matched to a free-form object surface, we suggest to employ the optic flow in order to compute additional point correspondences, Optic flow estimation is a mature research field with sophisticated algorithms available. Using here a high quality method ensures a reliable matching, In our experiments we demonstrate the performance of our method and in particular the improvements due to the optic flow.
UR - http://www.scopus.com/inward/record.url?scp=33745869797&partnerID=8YFLogxK
U2 - 10.1007/11744047_8
DO - 10.1007/11744047_8
M3 - Conference contribution
AN - SCOPUS:33745869797
SN - 3540338349
SN - 9783540338345
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 98
EP - 111
BT - Computer Vision - ECCV 2006, 9th European Conference on Computer Vision, Proceedings
Y2 - 7 May 2006 through 13 May 2006
ER -