Abstract
We propose a model for view-based adaptive affine tracking of moving objects. We avoid the need for feature-based matching in establishing correspondences through learning landmarks. We use an effective bootstrapping process based on colour segmentation and selective attention. We recover affine parameters with dynamic updates to the eigenspace using most recent history and perform predictions in parameter space. Experimental results are given to illustrate our approach.
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References
D. Beymer and T. Poggio, “Image representations for visual learning,” Science, vol. 272, pp. 1905–1909, June 1996.
S. McKenna, S. Gong, R. Wurtz, J. Tanner, and D. Banin, “Tracking facial feature points with gabor wavelets and shape models,” in IAPR International Conference on Audio-Video Based Biometric Person Authentication, Crans-Montana, Switzerland, March 1997.
S. McKenna and S. Gong, “Real-time face pose estimation,” Real Time Imaging, 1998, To appear in the Special Issue on Real-time Visual Monitoring and Inspection.
G. Hager and P. Belhumeur, “Real-time tracking of image region with changes in geometry and illumination,” in IEEE Conference on Computer Vision and Pattern Recognition, 1996.
M. Black and Y.Yacoob, “Eigen tracking: Robust matching and tracking of articulated objects using a view-based representation,” in European Conference on Computer Vision, Cambridge, England, April 1996.
P. Huber, Robust statistics, John Wiley and Sons, 1981.
S. Geman and D. McClure, “Statistical methods for tomographic image reconstruction,” Bull. Int. Statis. Inst., pp. 5–21, 1987.
M.J. Black and P. Anandan, “The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields,” CVIU, vol. 63, no. 1, pp. 75–104, 1996.
N. Sumpter, R. Boyle, and R. Tillett, “Modelling collective animal behaviour using extended point distribution models,” in British Machine Vision Conference, Colchester, September 1997, pp. 242–251.
I. Craw, “A manifold model of face and object recognition,” in Cognitive and Computational Aspects of Face Recognition, T. R. Valentine, Ed., pp. 183–203. Routledge, 1995.
Y. Raja, S. McKenna, and S. Gong, “Tracking and segmenting people in varying lighting conditions using colour,” in IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan, April 1998.
F. De la Torre, E. Martinez, E. Santamaria, and J.A Morin, “Moving object detection and tracking system: a real-time implementation,” in GRETSI, Grenoble, 1997, pp. 375–378.
H. Murase and S. Nayar, “Detection of 3d objects in cluttered scenes using hierarchical eigenspace,” Pattern Recognition Letters, vol. 18, pp. 375–384, 1997.
S. Nene and S. Nayar, “A simple algorithm for nearest neighbor search in high dimensions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 9, 1997.
S. Kay, Fundamentals of statistical signal processing: Estimation theory, Prentice Hall, 1993.
Z. Zhang, “Parameter estimation techniques: A tutorial with application to conic fitting,” Image and Vision Computing, 1996.
Y. Raja, S. McKenna, and S. Gong, “Colour model selection and adaptation in dynamic scenes,” in European Conference on Computer Vision, Freiburg, Germany, June 1998.
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© 1998 Springer-Verlag Berlin Heidelberg
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de la Torre, F., Gong, S., McKenna, S. (1998). View-based adaptive affine tracking. In: Burkhardt, H., Neumann, B. (eds) Computer Vision — ECCV'98. ECCV 1998. Lecture Notes in Computer Science, vol 1406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0055707
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DOI: https://doi.org/10.1007/BFb0055707
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