Image analysis – Applications – Target tracking or detecting
Reexamination Certificate
2004-06-14
2009-11-17
Kim, Charles (Department: 2624)
Image analysis
Applications
Target tracking or detecting
C348S169000
Reexamination Certificate
active
07620202
ABSTRACT:
A system for estimating orientation of a target based on real-time video data uses depth data included in the video to determine the estimated orientation. The system includes a time-of-flight camera capable of depth sensing within a depth window. The camera outputs hybrid image data (color and depth). Segmentation is performed to determine the location of the target within the image. Tracking is used to follow the target location from frame to frame. During a training mode, a target-specific training image set is collected with a corresponding orientation associated with each frame. During an estimation mode, a classifier compares new images with the stored training set to determine an estimated orientation. A motion estimation approach uses an accumulated rotation/translation parameter calculation based on optical flow and depth constrains. The parameters are reset to a reference value each time the image corresponds to a dominant orientation.
REFERENCES:
patent: 5454043 (1995-09-01), Freeman
patent: 5581276 (1996-12-01), Cipolla et al.
patent: 5594469 (1997-01-01), Freeman et al.
patent: 6002808 (1999-12-01), Freeman
patent: 6057909 (2000-05-01), Yahav et al.
patent: 6128003 (2000-10-01), Smith et al.
patent: 6215890 (2001-04-01), Matsuo et al.
patent: 6278798 (2001-08-01), Rao
patent: 6674904 (2004-01-01), McQueen
patent: 6720949 (2004-04-01), Pryor et al.
patent: 6788809 (2004-09-01), Grzeszczuk et al.
patent: 6804391 (2004-10-01), Blake et al.
patent: 6819782 (2004-11-01), Imagawa et al.
patent: 7203356 (2007-04-01), Gokturk et al.
patent: 2002/0041327 (2002-04-01), Hildreth et al.
patent: 2002/0086272 (2002-07-01), Ho et al.
patent: 2002/0181773 (2002-12-01), Higaki et al.
patent: 2003/0113018 (2003-06-01), Nefian et al.
patent: 2003/0156756 (2003-08-01), Gokturk et al.
patent: 2003/0169906 (2003-09-01), Gokturk et al.
patent: 2004/0151366 (2004-08-01), Nefian et al.
patent: 2004/0189720 (2004-09-01), Wilson et al.
patent: 2004/0193413 (2004-09-01), Wilson et al.
patent: 2005/0265583 (2005-12-01), Covell et al.
patent: 2006/0033713 (2006-02-01), Pryor
patent: 2006/0187305 (2006-08-01), Trivedi et al.
patent: 2001-143075 (2001-05-01), None
patent: 2001-307090 (2001-11-01), None
patent: 2002-216129 (2002-08-01), None
patent: WO 00/30023 (2000-05-01), None
patent: WO 2004/097612 (2004-11-01), None
International Search Report and Written Opinion of the International Searching Authority, PCT/IB2004/003149, Feb. 1, 2005.
Fujimura, Kikuo et al., “Real-time Pose classification for Driver Monitoring,”IEEE Intelligent Transportation Systems, 5thInternational Conference, 2002, pp. 174-178.
Ahlberg, J., “Using the Active Appearance Algorithm for Face and Facial Feature Tracking”, Proc 2nd Int. Workshop Recognition, Analysis, and Tracking of Faces and Gestures in Realtime Systems (RATFFG-RTS), Vancouver, Canada, Jul. 2001, pp. 68-72.
Athitsos, V., et al., “An Appearance-Based Framework for 3D Hand Shape Classification and Camera Viewpoint Estimation,” Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition (FGR'02), IEEE, May 20-21, 2002, 8 pages.
Beymer, D.J., “Face Recognition Under Varying Pose”, A.I. Memo No. 1461, Artificial Intelligence Laboratory, MIT, 1993 6 pages.
Birchfield, S., “An Elliptical Head Tracker”, Proc. 31st Asilomar Conf. Signals, Systems, and Computers, Nov. 1997, 5 pages.
Bretzner, L., et al., “Hand Gesture Recognition Using Multi-Scale Colour Features, Hierarchical Models and Particle Filtering,” Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition (FGR'02), IEEE, May 20-21, 2002, 8 pages.
Cascia, M. L. et al., “Fast, Reliable Head Tracking Under Varying Illumination: An Approach Based on Registration of Texture-Mapped 3D Models”, IEEE Trans. on Pattern Analaysis and Machine Intelligence, Apr. 2000, pp. 322-336, vol. 22, No. 4.
Comaniciu, D. et al., “Mean Shift: a Robust Approach Toward Feature Space Analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, May 2002, pp. 1-18, vol. 24, No. 5.
Duda, R.O. et al., “Pattern Classification,” John Wiley & Sons, Inc., 2001, 28 pages.
Edwards, J., “Learning to Identify and Track Faces in Image Sequences”, In Proceedings of the International Conference on Face and Gesture Recognition, 1998, pp. 260-265.
Feris, R. S., “Tracking Facial Features Using Gabor Wavelet Networks”, Proc. Brazilian Conference on Computer Graphics, Image Processing-Sibgrapi00, IEEE Computer Society Press, 2000, pp. 22-27.
Fujimura, K. et al., “Sign Recognition Using Depth Image Streams,” Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition (FGR 2006), Apr. 10-12, 2006, pp. 381-386.
Fukunaga, K., Introduction to Statistical Pattern Recognition, Academic Press, Boston, 1990, 306 pages.
Gavrila, D. M., et al., “Real-Time Object Detection for “Smart” Vehicles,” The Proceedings of the Seventh IEEE International Conference on Computer Vision, Sep. 20-27, 1999, 10 pages.
Gee et al., “Determining the Gaze of Faces in Images”, Image and Vision Computing, Dec. 10, 1994, pp. 639-647, vol. 12, No. 10.
Gong, S. et al., Dynamic Vision: From Images to Face Recognition, Imperial College Press, 2000, pp. 81-102.
Graham, D. B., “Characterizing Virtual Eigensignatures for General Purpose Face Recognition”, in Face Recognition: From Theory and Application (ed. H. Wechsler, et al , Springer, 1998 pp. 446-456.
Gvili, R., et al., “Depth Keying,” SPIE Elec. Imaging, 2003, 11 pages.
Henry, M.H., “Optical Flow Prepossessing For Pose Classification And Transition Recognition Using Class-Specific Principle Component Analysis”, Proc. Of IEEE Intelligent Vehicles Symposium, Jun. 2003, Columbus OH, pp. 604-609.
Huang, K.S., “Driver's View And Vehicle Surround Estimation Using Omnidirectional Video Stream”, Proc. IEEE Intelligent Vehicles Symposium, Jun. 9-11, 2003, Columbus OH, pp. 444-449.
Huttenlocher, D., “Comparing Images Using The Hausdorff Distance”, IEEE Transactions On Pattern Analysis And Machine Intelligence, Sep. 1993, pp. 850-863, vol. 15, No. 9.
Iddan, G.J., “3D Imaging in the Studio”, SPIE, Apr. 2001, pp. 48-55, vol. 4298.
Ishibuchi, K., et al., “Real Time Vision-Based Hand Gesture Estimation for a Human-Computer Interface,” Systems and Computers in Japan, 1997, pp. 76-88, vol. 28, No. 7.
Ji, Q. et al., “Real Time 3D Face Pose Discrimination Based on Active IR Illumination”, Int. Conf. on Pattern Recognition, Quebec, Canada, Aug. 2002, pp. 310-313.
Jojic, N., et al., “Detection and Estimation of Pointing Gestures in Dense Disparity Maps,” Fourth IEEE International Conference on Automatic Face and Gesture Recognition, Mar. 28-30, 2000, 15 pages.
Kamiji, K. et al., “Study Of Airbag Interference With Out Of Position Occupant By the Computer Simulation”, 2000, 7 pages.
Krueger, V.et al. “Efficient Head Pose Estimation With Gabor Wavelet Networks”,BMVC, 2000, 11 pages.
Lee, C., et al., “Online, Interactive Learning of Gestures for Human/Robot Interfaces,” Proceedings of the 1996 IEEE International Conference on Robotics and Automation, Apr. 1996, pp. 2982-2987.
Liu, X. et al., “Hand Gesture Recognition using Depth Data,” Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, (FGR 2004), 2004, pp. 529-534.
Malassiotis, S., et al., “A Gesture Recognition System Using 3D Data,” Proceedings of the First International Symposium on 3D Data Processing Visualization and Transmission (3DPVT'02), IEEE, 2002, pp. 1-4.
Malciu, M. et al., “A Robust Model-Based Approach For 3D Head Tracking In Video Sequences”, International Conference On Automatic Face And Gesture Recognition, 2000,
Fujimura Kikuo
Zhu Youding
Duell Mark E.
Fenwick & West LLP
Honda Motor Co. Ltd.
Kim Charles
The Ohio State University Research Foundation
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