Abstract
In this paper, we propose a statistical model-based contour tracking algorithm based on the Condensation framework. The models include a novel object shape prediction model and two statistical object models. The object models consist of the grayscale histogram and contour shape PCA models computed from the previous tracking results. With the incremental singular value decomposition (SVD) technique, these three models are learned and updated very efficiently during tracking. We show that the proposed shape prediction model outperforms the affine predictor through experiments. Experimental results show that the proposed contour tracking algorithm is very stable in tracking human heads on real videos with object scaling, rotation, partial occlusion, and illumination changes.
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References
Isard, M., Blake, A.: CONDENSATION - conditional density propagation for visual tracking. International Journal of Computer Vision 29, 5–28 (1998)
Jepson, A.D., Fleet, D.J., El-Maraghi, T.F.: Robust online appearance models for visual tracking. In: IEEE Conf. Compute Vision Pattern Recognition, vol. 1, pp. 415–422 (2001)
Lim, J., Ross, D., Lin, R.S., Yang, M.H.: Incremental learning for visual tracking. In: Neural Information Processing Systems 17(NIPS 2004) (2004)
Liu, T.-L., Chen, H.-T.: Real-time tracking using trust-region methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 397–402 (2004)
Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proc. Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 142–149 (2000)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 564–575 (2003)
Levy, A., Lindenbaum, M.: Sequential Karhunen Loeve basis extraction and its application to image. IEEE Transactions on Image Processing 9, 1371–1374 (2000)
Rui, Y., Chen, Y.: Better Proposal Distributions: Object Tracking Using Unscented Particle Filter. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 786–793 (2001)
Maggio, E., Cavallaro, A.: Hybrid Particle Filter and Mean Shift tracker with adaptive transition model. In: Proc. of IEEE Signal Processing Society International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 19–23 (2005)
Okuma, K., Taleghani, A., de Freitas, N., Little, J.J., Lowe, D.G.: A Boosted Particle Filter: Multi-target Detection and Tracking. In: European Conference on Computer Vision, pp. 28–39 (2004)
Nummiaro, K., Koller-Meier, E., Van Gool, L.: An Adaptive Color-Based Particle Filter. International Journal of Image and Vision Computing 21, 99–110 (2003)
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© 2006 Springer-Verlag Berlin Heidelberg
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Chang, K., Lai, SH. (2006). Adaptive Object Tracking with Online Statistical Model Update. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_37
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DOI: https://doi.org/10.1007/11612704_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31244-4
Online ISBN: 978-3-540-32432-4
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