Abstract
Automatic eigentemplate learning is discussed for a sparse template tracker. Using an eigentemplate learned from multiple sequences, a sparse template tracker can efficiently track a target that changes appearance. The present paper provides a feasible solution for eigentemplate learning when multiple image sequences are available. Two types of eigentemplates are compared in the present paper, namely, a single eigentemplate, and a set of directional eigentemplates. The single eigentemplate simply consists of all images learned from multiple sequences.On the other hand, directional eigentemplates are obtained by decomposing the single eigentemplate into three directions of the face poses. The sparse template tracker is also expanded to directional eigentemplates.Finally, the effectiveness of the provided solution is demonstrated in the learning and tracking experiments. The experimental results indicate that directional learning works well with small seed data,and that the directional eigentracker works better than the single eigentracker.
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References
Jepson, A.D., Fleet, D.J., El-Maraghi, T.F.: Robust online appearance models for visual tracking. IEEE Trans. Pattern Analysis and Machine Intelligence 25(10), 1296–1311 (2003)
Isard, M., Blake, A.: Condensation – conditional density propagation for visual tracking. International Journal of Computer Vision 29(1), 5–28 (1998)
Williams, O., Blake, A., Cipolla, R.: A sparse probabilistic learning algorithm for real-time tracking. In: Proc. ICCV, pp. 353–360 (2003)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)
Black, M., Jepson, A.: Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. International Journal of Computer Vision 26(1), 63–84 (1998)
Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Analysis and Machine Intelligence 29(2), 261–271 (2007)
Cascia, M.L., Sclaroff, S., Athitsos, V.: Fast,reliable head tracking under varying illumination: An approach based on robust registration of texture-mapped 3d models. IEEE Trans. Pattern Analysis and Machine Intelligence 22(4), 322–336 (2000)
Matsubara, Y., Shakunaga, T.: Sparse template matching and its application to real-time object tracking. IPSJ Transactions on Computer Vision and Image Media 46, 60–71 (2005) (in japanese no.sig9(cvim11))
Satake, J., Shakunaga, T.: Multiple target tracking by appearance-based condensation tracker using structure information. In: Proc. International Conference on Production Research, vol. 3, pp. 294–297 (2004)
Shakunaga, T., Matsubara, Y., Noguchi, K.: Appearance tracker based on sparse eigentemplate. In: Proc. Int’l Conf. on Machine Vision & Applications, pp. 13–17 (2005)
Oka, Y., Kuroda, T., Migita, T., Shakunaga, T.: Tracking 3d pose of rigid object by sparse template matching. In: Proc. the 5th International Conference on Image and Graphics, ICIG 2009, pp. 390–397 (2009)
Mikami, D., Otsuka, K., Yamato, J.: Memory-based particle filter for face pose tracking robust under complex dynamics. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp. 999–1006 (2009)
Mikami, D., Otsuka, K., Yamato, J.: Memory-Based Particle Filter for Tracking Objects with Large Variation in Pose and Appearance. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 215–228. Springer, Heidelberg (2010)
Murphy-Chutorian, E., Trivedi, M.M.: Hyhope: Hybrid head orientation and position estimation for vision-based driver head tracking. IEEE Trans. Pattern Analysis and Machine Intelligence 31(4), 607–626 (2009)
Oka, Y., Shakunaga, T.: Sparse eigentracker augmented by associative mapping to 3d shape. In: Proc. IEEE Conference on Automatic Face and Gesture Recognition (FG 2011), pp. 649–656 (2011)
Shakunaga, T., Noguchi, K.: Robust tracking of appearance by sparse template adaptation. In: Proc. 8th IASTED Int’l Conf. on Signal and Image Processing, pp. 85–90 (2006)
Sakabe, K., Taguchi, T., Shakunaga, T.: Automatic Eigentemplate Learning for Sparse Template Tracker. In: Wada, T., Huang, F., Lin, S. (eds.) PSIVT 2009. LNCS, vol. 5414, pp. 714–725. Springer, Heidelberg (2009)
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Seto, H., Taguchi, T., Shakunaga, T. (2011). Directional Eigentemplate Learning for Sparse Template Tracker. In: Ho, YS. (eds) Advances in Image and Video Technology. PSIVT 2011. Lecture Notes in Computer Science, vol 7088. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25346-1_10
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DOI: https://doi.org/10.1007/978-3-642-25346-1_10
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