Abstract
We propose an exemplar-based tracking framework for human contour tracking. The exemplars, i.e. the contour representatives, are used to construct a contour ensemble. The main task of contour ensemble is to generate contours to fill in the gaps in-between in the test sequences, and to supply the dynamics for updating the target contour by fast contour query. As a result, a normal dynamic Bayesian network is only used to infer the location and the size of the target contour. The effectiveness of the proposed method is tested by many human motion sequences.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Isard, M., Blake, A.: Condensation conditional-density propagation for visual tracking. Jour. of Computer Vision 29, 5–28 (1998)
Doucet, A., De Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer, Heidelberg (2001)
Gavrila, D., Philomin, V.: Real-time object detection for smart vehicles. In: Proc. of IEEE Int. Conf. on Computer Vision (ICCV), Corfu, Greece, pp. 87–93 (1999)
Frey, B.J., Jojic, N.: Learning graphical models of images, videos and their spatial transformations. In: Proc. of the Conf. on Uncertainty in Artificial Intelligence, Stanford, USA, pp. 184–191 (2000)
Toyama, K., Blake, A.: Probabilistic tracking with exemplars in a metric space. Jour. of Computer Vision 48, 9–19 (2002)
Cootes, T.F., Cooper, D., Taylor, C., et al.: Active shape models, their training and application. Computer Vision and Image Understanding 61, 38–59 (1995)
Wu, Y., Hua, G., Yu, T.: Switching observation models for contour tracking in clutter. In: Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), Madison Wisconsin, pp. 295–302 (2003)
Wang, Q., Xu, G.Y., Ai, H.Z.: Learning object intrinsic structure for robust visual tracking. In: CVPR, Madison Wisconsin, pp. 227–233 (2003)
Pavlovic, V., Sharma, R., Cham, T.J., Murphy, K.P.: A dynamic bayesian network approach to figure tracking using learned dynamic models. In: ICCV, Corfu, Greece, pp. 94–101 (1999)
Isard, M., Blake, A.: A mixed-state condesation tracker with automatic model-switching. In: ICCV, Bombay, India, pp. 94–101 (1998)
MacCormick, J., Blake, A.: A probabilistic contour discriminant for object localization. In: ICCV, Bombay, India, pp. 390–395 (1998)
MacCormick, J., Blake, A.: A probabilistic exclusion principle for tracking multiple objects. In: ICCV, Corfu, Greece, pp. 572–578 (1999)
Chen, Y.Q., Rui, Y., Huang, T.: Jpdaf based hmm or real-time contour tracking. In: CVPR, Cambridge, MA, pp. 543–550 (2001)
Shen, C.H., van den Hengel, A., Dick, A.: Probabilistic multiple cue integration for particle filter based tracking. In: Proc. of Digital Image Computing: Techniques and Applications, Sydney, Australia, pp. 399–408 (2003)
Tomasi, C., Petrov, S., Sastry, A.: 3d tracking = classification + interpolation. In: ICCV, Pairs, France, pp. 1441–1448 (2003)
Efros, A., Leung, T.: Texture synthesis by non-parametric sampling. In: ICCV, Corfu, Greece, pp. 1033–1038 (1999)
Sethian, A.: Level Set Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision and Materials Sciences. Cambridge University Press, Cambridge (1996)
Chui, H., Rangarajan, A.: A new algorithm for non-rigid point matching. In: CVPR, Hilton Head Island, SC, pp. 44–51 (2000)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. On Pattern Analysis and Machine Intelligence (PAMI) 25, 564–577 (2003)
Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.A.: Comparing images using the hausdorff distance. PAMI 15, 850–863 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xiang, S., Nie, F., Zhang, C. (2006). Exemplar-Based Human Contour Tracking. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_35
Download citation
DOI: https://doi.org/10.1007/11612032_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31219-2
Online ISBN: 978-3-540-32433-1
eBook Packages: Computer ScienceComputer Science (R0)