Abstract
The research on tracking templates or image patches in a sequence of images has been largely dominated by energy-minimization-based methods. However, since its introduction in Jurie and Dhome (IEEE Trans Pattern Anal Mach Intell, 2002), the learning-based approach called linear predictors has proven to be an efficient and reliable alternative for template tracking, demonstrating superior tracking speed and robustness. But, their time intensive learning procedure prevented their use in applications where online learning is essential. Indeed, Holzer et al. (Adaptive linear predictors for real-time tracking, 2010) presented an iterative method to learn linear predictors; but it starts with a small template that makes it unstable at the beginning. Therefore, we propose three methods for highly efficient learning of full-sized linear predictors—where the first one is based on dimensionality reduction using the discrete cosine transform; the second is based on an efficient reformulation of the learning equations; and, the third is a combination of both. They show different characteristics with respect to learning time and tracking robustness, which makes them suitable for different scenarios.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
See version \(0.4\) available at http://esm.gforge.inria.fr/ESM.html.
References
Baker, S., & Matthews, I. (2001). Equivalence and efficiency of image alignment algorithms. In Conference on Computer Vision and Pattern Recognition.
Baker, S., & Matthews, I. (2004). Lucas-kanade 20 years on: A unifying framework. International Journal of Computer Vision, 56(3), 221–255.
Ben-Israel, A., & Greville, T. N. (2003). Generalized inverses. Berlin: Springer.
Benhimane, S., & Malis, E. (2007). Homography-based 2d visual tracking and servoing. International Journal of Robotics Research, 26(7), 661–676.
Cascia, M., Sclaroff, S., & Athitsos, V. (2000). Fast, reliable head tracking under varying illumination: An approach based on registration of texture-mapped 3d models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 322–336.
Dame, A., & Marchand, E. (2010). Accurate real-time tracking using mutual information. In 2010 9th IEEE International Symposium on Mixed and Augmented Reality (ISMAR).
Dellaert, F., & Collins, R. (1999). Fast image-based tracking by selective pixel integration. In ICCV Workshop of Frame-Rate Vision.
Grabner, H., Leistner, C., & Bischof, H. (2008). Semi-supervised on-line boosting for robust tracking. In: D. Forsyth, P. Torr & A. Zisserman (Eds.), Computer vision ECCV 2008. Lecture notes in computer science (Vol. 5302, pp. 234–247). Berlin Heidelberg: Springer.
Gräßl, C., Zinßer, T., & Niemann, H. (2003). Illumination insensitive template matching with hyperplanes. In Proceedings of Pattern recognition: 25th DAGM Symposium.
Gräßl, C., Zinßer, T., & Niemann, H. (2004). Efficient hyperplane tracking by intelligent region selection. In Image Analysis and Interpretation.
Hager, G., & Belhumeur, P. (1998). Efficient region tracking with parametric models of geometry and illumination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(10), 1025–1039.
Hinterstoisser, S., Benhimane, S., Navab, N., Fua, P., & Lepetit, V. (2008). Online learning of patch perspective rectification for efficient object detection. In Conference on Computer Vision and Pattern Recognition.
Hinterstoisser, S., Holzer, S., Cagniart, C., Ilic, S., Konolige, K., Navab, N., & Lepetit, V. (2011). Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes. In IEEE International Conference on Computer Vision (ICCV).
Hinterstoisser, S., Kutter, O., Navab, N., Fua, P., & Lepetit, V. (2009). Real-time learning of accurate patch rectification. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Hinterstoisser, S., Lepetit, V., Ilic, S., Fua, P., & Navab, N. (2010). Dominant orientation templates for real-time detection of texture-less objects. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., & Navab, N. (2012). Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes. In Asian Conference on Computer Vision.
Holzer, S., Hinterstoisser, S., Ilic, S., & Navab, N. (2009). Distance transform templates for object detection and pose estimation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Holzer, S., Ilic, S., & Navab, N. (2010). Adaptive linear predictors for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Holzer, S., Ilic, S., & Navab, N. (2013). Multi-layer adaptive linear predictors for real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1), 105–117.
Holzer, S., Ilic, S., Tan, D., & Navab, N. (2012). Efficient learning of linear predictors using dimensionality reduction. In Asian Conference on Computer Vision.
Holzer, S., Pollefeys, M., Ilic, S., Tan, D.J., & Navab, N. (2012). Online learning of linear predictors for real-time tracking. In 12th European Conference on Computer Vision (ECCV).
Jurie, F., & Dhome, M. (2002). Hyperplane approximation for template matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 996–1000.
Jurie, F., & Dhome, M. (2002). Real time robust template matching. In British Machine Vision Conference.
Kalal, Z., Mikolajczyk, K., & Matas, J. (2012). Tracking-learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(7), 1409–1422.
Klein, G., & Murray, D. (2007). Parallel tracking and mapping for small ar workspaces. In 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, 2007. ISMAR 2007, pp. 225–234.
Klein, G., & Murray, D. (2008). Improving the agility of keyframe-based slam. Computer Vision ECCV 2008. Lecture Notes in Computer Science (Vol. 5303, pp. 802–815). Berlin Heidelberg: Springer.
Lieberknecht, S., Benhimane, S., Meier, P., Navab, N. (2009). A dataset and evaluation methodology for template-based tracking algorithms. In ISMAR, pp. 145–151.
Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.
Lucas, B., & Kanade, T. (1981) An Iterative Image Registration Technique with an Application to Stereo Vision. In International Joint Conference on Artificial Intelligence.
Malis, E. (2004). Improving vision-based control using efficient second-order minimization techniques. In IEEE International Conference on Robotics and Automation.
Matas, J., Zimmermann, K., Svoboda, T., Hilton, A. (2006). Learning efficient linear predictors for motion estimation. In Computer Vision, Graphics and Image Processing.
Mayol, W.W., & Murray, D.W. (2008). Tracking with general regression. Journal of Machine Vision and Applications, 19(1), 65–72.
Özuysal, M., Fua, P., Lepetit, V. (2007). Fast Keypoint Recognition in Ten Lines of Code. In Conference on Computer Vision and Pattern Recognition.
Parisot, P., Thiesse, B., & Charvillat, V. (2007). Selection of reliable features subsets for appearance-based tracking. In Signal-Image Technologies and Internet-Based System, 16–18 Dec 2007, pp. 891–898.
Richa, R., Sznitman, R., Taylor, R., Hager, G. (2011). Visual tracking using the sum of conditional variance. In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Shum, H.Y., & Szeliski, R. (2000). Construction of panoramic image mosaics with global and local alignment. International Journal of Computer Vision, 36(2), 101–130.
Vacchetti, L., Lepetit, V., & Fua, P. (2004). Stable real-time 3d tracking using online and offline information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(10), 1385–1391.
Zimmermann, K., Matas, J., & Svoboda, T. (2009). Tracking by an optimal sequence of linear predictors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(4), 677–692.
Acknowledgments
This work was partly funded by Willow Garage, Inc., California, USA.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by Cordelia Schmid.
Rights and permissions
About this article
Cite this article
Holzer, S., Ilic, S., Tan, D. et al. Efficient Learning of Linear Predictors for Template Tracking. Int J Comput Vis 111, 12–28 (2015). https://doi.org/10.1007/s11263-014-0729-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11263-014-0729-1