Abstract
Existing multi-kernel tracking methods are based on a forwards additive motion model formulation. However this approach suffers from the need to estimate an update matrix for each iteration. This paper presents a general framework that extends the existing approach and that allows to introduce a new inverse compositional formulation which shifts the computation of the update matrix to a one time initialisation step. The proposed approach thus reduces the computational complexity of each iteration, compared to the existing forwards approach. The approaches are compared both in terms of algorithmic complexity and quality of the estimation.
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References
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 564–575 (2003)
Fan, Z., Wu, Y., Yang, M.: Multiple collaborative kernel tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, pp. 502–509 (2005)
Hager, G.D., Dewan, M., Stewart, C.V.: Multiple kernel tracking with SSD. In: IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, pp. 790–797 (2004)
Georgescu, B., Meer, P.: Point matching under large image deformations and illumination changes. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 674–688 (2004)
Baker, S., Matthews, I.: Lucas-kanade 20 years on: A unifying framework. International Journal of Computer Vision 56, 221–255 (2004)
Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)
Elgammal, A.M., Duraiswami, R., Davis, L.S.: Probabilistic tracking in joint feature-spatial spaces. In: IEEE Conference on Computer Vision and Pattern Recognition, Madison, WI, USA, pp. 781–788 (2003)
Zhang, H., Huang, W., Huang, Z., Li, L.: Affine object tracking with kernel-based spatial-color representation. In: IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, pp. 293–300 (2005)
Guskov, I.: Kernel-based template alignment. In: IEEE Conference on Computer Vision and Pattern Recognition, New-York, USA, pp. 610–617 (2006)
Liu, T.L., Chen, H.T.: Real-time tracking using trust-region methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 397–402 (2003)
Baker, S., Matthews, I.: Equivalence and efficiency of image alignment algorithms. In: IEEE Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA, pp. 1090–1097 (2001)
Jurie, F., Dhome, M.: Hyperplane approximation for template matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 996–1000 (2002)
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Megret, R., Mikram, M., Berthoumieu, Y. (2006). Inverse Composition for Multi-kernel Tracking. In: Kalra, P.K., Peleg, S. (eds) Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science, vol 4338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949619_43
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DOI: https://doi.org/10.1007/11949619_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68301-8
Online ISBN: 978-3-540-68302-5
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