Method for tracking objects in videos using covariance matrices

Image analysis – Applications – Target tracking or detecting

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C382S190000, C382S191000

Reexamination Certificate

active

07620204

ABSTRACT:
A method is provided for tracking a non-rigid object in a sequence of frames of a video. Features of an object are extracted from the video. The features include locations of pixels and properties of the pixels. The features are used to construct a covariance matrix. The covariance matrix is used as a descriptor of the object for tracking purposes. Object deformations and appearance changes are managed with an update mechanism that is based on Lie algebra averaging.

REFERENCES:
patent: 2003/0107653 (2003-06-01), Utsumi et al.
patent: 2005/0129278 (2005-06-01), Rui et al.
patent: 2006/0013454 (2006-01-01), Flewelling et al.
patent: 2007/0092110 (2007-04-01), Xu et al.
Y. Boykov and D. Huttenlocher. Adaptive bayesian recognition in tracking rigid objects. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Hilton Head, SC, vol. II, pp. 697-704, 2000.
T.-J. Cham and J. M. Rehg. A multiple hypothesis approach to figure tracking. In Proc. Perceptual User Interfaces, pp. 19-24, 1998.
D. Comaniciu, V. Ramesh, and P. Meer. Real-time tracking of non-rigid objects using mean shift. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Hilton Head,SC, vol. 1, pp. 142-149, 2000.
P. Fletcher, C. Lu, and S. Joshi. Statistics of shape via principal geodesic analysis on lie groups. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Madison, WI, vol. 1, pp. 95-101, 2003.
W. F..orstner and B. Moonen. A metric for covariance matrices. Technical report, Dept. of Geodesy and Geoinformatics, Stuttgart University, 1999.
V. Govindu. Lie-algebraic averaging for globally consistent motion estimation. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Washington, DC, vol. 1, pp. 684-691, 2003.
B. Han, Y. Zhu, D. Comanicu, and L. Davis. Kernel-based bayesian filtering for object tracking. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, San Diego, CA, 2005.
M. Isard and I. Blake. Condensation—conditional density propagation for visual tracking. In Intl. J. of Computer Vision, vol. 29, pp. 5-28, 1998.
F. Porikli. Integral histogram: A fast way to extract histograms in cartesian spaces. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, San Diego, CA, 2005.
O. Tuzel, R. Subbarao, and P. Meer. Simultaneous multiple 3d motion estimation via mode finding on lie groups. In Proc. 10th Intl. Conf. on Computer Vision, Beijing, China, vol. I, pp. 18-25, 2006.

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Method for tracking objects in videos using covariance matrices does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Method for tracking objects in videos using covariance matrices, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method for tracking objects in videos using covariance matrices will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-4066439

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.