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Issue No.01 - Jan. (2013 vol.35)
pp: 105-117
S. Holzer , Dept. of Comput. Sci., Tech. Univ. of Munich (TUM), Garching, Germany
S. Ilic , Dept. of Comput. Sci., Tech. Univ. of Munich (TUM), Garching, Germany
N. Navab , Dept. of Comput. Sci., Tech. Univ. of Munich (TUM), Garching, Germany
ABSTRACT
Enlarging or reducing the template size by adding new parts or removing parts of the template according to their suitability for tracking requires the ability to deal with the variation of the template size. For instance, real-time template tracking using linear predictors, although fast and reliable, requires using templates of a fixed size and does not allow online modification of the predictor. To solve this problem, we propose the Adaptive Linear Predictors (ALPs), which enable fast online modifications of prelearned linear predictors. Instead of applying a full matrix inversion for every modification of the template shape, as standard approaches to learning linear predictors do, we just perform a fast update of this inverse. This allows us to learn the ALPs in a much shorter time than standard learning approaches while performing equally well. Additionally, we propose a multilayer approach to detect occlusions and use ALPs to effectively handle them. This allows us to track large templates and modify them according to the present occlusions. We performed an exhaustive evaluation of our approach and compared it to standard linear predictors and other state-of-the-art approaches.
INDEX TERMS
Tracking, Vectors, Robustness, Pattern analysis, Shape, Artificial intelligence,linear predictors, Template tracking
CITATION
S. Holzer, S. Ilic, N. Navab, "Multilayer Adaptive Linear Predictors for Real-Time Tracking", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 1, pp. 105-117, Jan. 2013, doi:10.1109/TPAMI.2012.86
REFERENCES
[1] B. Lucas and T. Kanade, "An Iterative Image Registration Technique with an Application to Stereo Vision," Proc. Seventh Int'l Joint Conf. Artificial Intelligence, pp. 674-679, Aug. 1981.
[2] H.-Y. Shum and R. Szeliski, "Construction of Panoramic Image Mosaics with Global and Local Alignment," Int'l J. Computer Vision, vol. 36, no. 2, pp. 101-130, Feb. 2000.
[3] G. Hager and P. Belhumeur, "Efficient Region Tracking with Parametric Models of Geometry and Illumination," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 10, pp. 1025-1039, Oct. 1998.
[4] M. Cascia, S. Sclaroff, and V. Athitsos, "Fast, Reliable Head Tracking under Varying Illumination: An Approach Based on Registration of Texture-Mapped 3D Models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 4, pp. 322-336, Apr. 2000.
[5] F. Dellaert and R. Collins, "Fast Image-Based Tracking by Selective Pixel Integration," Proc. IEEE Int'l Conf. Computer Vision Workshop Frame-Rate Vision, pp. 1-22, Sept. 1999.
[6] S. Baker and I. Matthews, "Equivalence and Efficiency of Image Alignment Algorithms," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 1090-1097, Dec. 2001.
[7] S. Baker and I. Matthews, "Lucas-Kanade 20 Years On: A Unifying Framework," Int'l J. Computer Vision, vol. 56, pp. 221-255, Mar. 2004.
[8] E. Malis, "Improving Vision-Based Control Using Efficient Second-Order Minimization Techniques," Proc. IEEE Int'l Conf. Robotics and Automation, vol. 2, pp. 1843-1848, May 2004.
[9] S. Benhimane and E. Malis, "Real-Time Image-Based Tracking of Planes Using Efficient Second-Order Minimization," Proc. Conf. Intelligent Robots and Systems, vol. 1, pp. 943-948, Sept. 2004.
[10] S. Benhimane and E. Malis, "Homography-Based 2D Visual Tracking and Servoing," Int'l J. Robotics Research, vol. 26, no. 7, pp. 661-676, July 2007.
[11] F. Jurie and M. Dhome, "Hyperplane Approximation for Template Matching," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 996-100, July 2002.
[12] F. Jurie and M. Dhome, "Real Time Robust Template Matching," Proc. British Machine Vision Conf., pp. 123-131, Sept. 2002.
[13] C. Gräßl, T. Zinßer, and H. Niemann, "Efficient Hyperplane Tracking by Intelligent Region Selection," Proc. IEEE Sixth Southwest Image Analysis and Interpretation, pp. 51-55, Mar. 2004.
[14] P. Parisot, B. Thiesse, and V. Charvillat, "Selection of Reliable Features Subsets for Appearance-Based Tracking," Proc. Third IEEE Int'l Conf. Signal-Image Technologies and Internet-Based System, pp. 891-898, Dec. 2007.
[15] J. Matas, K. Zimmermann, T. Svoboda, and A. Hilton, "Learning Efficient Linear Predictors for Motion Estimation," Proc. Fifth Indian Conf. Computer Vision, Graphics and Image, pp. 445-456, Dec. 2006.
[16] W.W. Mayol and D.W. Murray, "Tracking with General Regression," J. Machine Vision and Applications, vol. 19, no. 1, pp. 65-72, Jan. 2008.
[17] K. Zimmermann, J. Matas, and T. Svoboda, "Tracking by an Optimal Sequence of Linear Predictors," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 4, pp. 677-692, Apr. 2009.
[18] S. Baker, R. Gross, I. Matthews, and T. Ishikawa, "Lucas-Kanade 20 Years On: A Unifying Framework: Part 2," Technical Report CMU-RI-TR-03-01, Robotics Inst., Feb. 2003.
[19] I. Patras and E. Hancock, "Regression-Based Template Tracking in Presence of Occlusions," Proc. Eighth Int'l Workshop Image Analysis for Multimedia Interactive Services, p. 15, June 2007.
[20] C. Gräßl, T. Zinßer, and H. Niemann, "Illumination Insensitive Template Matching with Hyperplanes," Proc. 25th DAGM Symp. Pattern Recognition, pp. 273-280, Sept. 2003.
[21] H.V. Henderson and S.R. Searle, "On Deriving the Inverse of a Sum of Matrices," SIAM Rev., vol. 23, no. 1, pp. 53-60, Jan. 1981.
[22] S. Hinterstoisser, S. Benhimane, N. Navab, P. Fua, and V. Lepetit, "Online Learning of Patch Perspective Rectification for Efficient Object Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, June 2008.
[23] S. Holzer, S. Ilic, and N. Navab, "Adaptive Linear Predictors for Real-Time Tracking," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1807-1814, June 2010.
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