Abstract
Hidden State Shape Models (HSSMs) were previously proposed to represent and detect objects in images that exhibit not just deformation of their shape but also variation in their structure. In this paper, we introduce Dynamic Hidden-State Shape Models (DHSSMs) to track and recognize the non-rigid motion of such objects, for example, human hands. Our recursive Bayesian filtering method, called DP-Tracking, combines an exhaustive local search for a match between image features and model states with a dynamic programming approach to find a global registration between the model and the object in the image. Our contribution is a technique to exploit the hierarchical structure of the dynamic programming approach that on average considerably speeds up the search for matches. We also propose to embed an online learning approach into the tracking mechanism that updates the DHSSM dynamically. The learning approach ensures that the DHSSM accurately represents the tracked object and distinguishes any clutter potentially present in the image. Our experiments show that our method can recognize the digits of a hand while the fingers are being moved and curled to various degrees. The method is robust to various illumination conditions, the presence of clutter, occlusions, and some types of self-occlusions. The experiments demonstrate a significant improvement in both efficiency and accuracy of recognition compared to the non-recursive way of frame-by-frame detection.
Chapter PDF
References
Bar-Shalom, Y., Fortmann, T.: Tracking and Data Association. Academic Press, London (1988)
Athitsos, V., Wang, J., Sclaroff, S., Betke, M.: Detecting instances of shape classes that exhibit variable structure. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 121–134. Springer, Heidelberg (2006)
Wang, J., Athitsos, V., Sclaroff, S., Betke, M.: Detecting objects of variable shape structure with hidden state shape models. IEEE T PAMI 30(3), 477–492 (2008)
Han, B., Zhu, Y., Comaniciu, D., Davis, L.: Kernel-based Bayesian filtering for object tracking. In: CVPR, vol. 1, pp. 227–234 (2005)
MacCormick, J., Isard, M.: Partitioned sampling, articulated objects, and interface-quality hand tracking. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 3–19. Springer, Heidelberg (2000)
Sudderth, E., Mandel, M., Freeman, W., Willsky, A.: Distributed occlusion reasoning for tracking with nonparametric belief propagation. In: NIPS (2004)
Sigal, L., Bhatia, S., Roth, S., Black, M.J., Isard, M.: Tracking loose-limbed people. In: CVPR, pp. 421–428 (2004)
Athitsos, V., Sclaroff, S.: Estimating 3d hand pose from a cluttered image. In: CVPR, pp. 432–440 (2003)
Shakhnarovich, G., Viola, P., Darrell, T.: Fast pose estimation with parameter-sensitive hashing. In: ICCV, pp. 750–758 (2003)
Stenger, B., Thayananthan, A., Torr, P., Cipolla, R.: Hand pose estimation using hierarchical detection. In: Proceeding of International Workshop on Human-Computer Interaction. LNCS (2004)
Forsyth, D., Arikan, O., Ikemoto, L., O’Brien, J., Ramanan, D.: Computational Studies of Human Motion: Part 1, Tracking and Motion Synthesis (2006)
Ramanan, D., Forsyth, D.A., Zisserman, A.: Strike a pose: Tracking people by finding stylized poses. In: CVPR, pp. 20–25 (2005)
Felzenszwalb, P.F., Schwartz, J.D.: Hierarchical matching of deformable shapes. In: CVPR, pp. 1–8 (2007)
Opelt, A., Pinz, A., Zisserman, A.: A boundary-fragment-model for object detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 575–588. Springer, Heidelberg (2006)
Shotton, J., Blake, A., Cipolla, R.: Contour-based learning for object detection. In: ICCV, pp. 503–510 (2005)
Heap, T., Hogg, D.: Wormholes in shape space: Tracking through discontinuous changes in shape. In: ICCV, pp. 344–349 (1998)
Rabiner, L.R.: A tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Collins, R., Liu, Y.: On-line selection of discriminative tracking features. In: ICCV, pp. 346–354 (2003)
Schmidt, F.R., Farin, D., Cremers, D.: Fast matching of planar shapes in sub-cubic runtime. In: ICCV, pp. 1–6 (October 2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Electronic Supplementary Material
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, Z., Betke, M., Wang, J., Athitsos, V., Sclaroff, S. (2008). Tracking with Dynamic Hidden-State Shape Models. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88682-2_49
Download citation
DOI: https://doi.org/10.1007/978-3-540-88682-2_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88681-5
Online ISBN: 978-3-540-88682-2
eBook Packages: Computer ScienceComputer Science (R0)