Abstract
We present a generative probabilistic model for 3D scenes with stereo views. With this model, we track an object in 3 dimensions while simultaneously learning its appearance and the appearance of the background. By using a generative model for the scene, we are able to aggregate evidence over time. In addition, the probabilistic model naturally handles sources of variability.
For inference and learning in the model, we formulate an Expectation Maximization (EM) algorithm where Rao-Blackwellized Particle filtering is used in the E step. The use of stereo views of the scene is a strong source of disambiguating evidence and allows rapid convergence of the algorithm. The update equations have an appealing form and as a side result, we give a generative probabilistic interpretation for the Sum of Squared Differences (SSD) metric known from the field of Stereo Vision.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Malciu, M.: A robust model-based approach for 3d head tracking in video sequences. In: Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (FG 2000), Grenoble, France, vol. 1, pp. 169–174 (2000)
Schodl, I., Haro, A.: Head tracking using a textured polygonal model. In. In: Proceedings of Workshop on Perceptual User Interfaces (1998)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 7–42 (2002)
Frey, B., Jojic, N.: Transformation-invariant clustering and dimensionality reduction using em. IEEE Transactions on Pattern Analysis and Machine Intelligence (2000)
Papanikolopoulos, N., Khosla, P., Kanade, T.: Vision and control techniques for robotic visual tracking. In: Proc. IEEE Int. Conf. Robotics and Autmation, vol. 1, pp. 851–856 (1991)
Toyama, K.: Prolegomena for robust face tracking. Technical Report MSR Technical Report, MSR-TR-98-65, Microsoft Research (1998)
Jebara, T., Azarbeyejani, A., Pentland, A.: 3d structure from 2d motion. IEEE Signal Processing Magazine 16 (1999)
Sun, J., Shum, H.Y., Zheng, N.N.: Stereo matching using belief propagation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 510–524. Springer, Heidelberg (2002)
Scharstein, D., Szeliski, R.: Stereo matching with non-linear diffusion. In: Proc. of IEEE conference on Computer Vision and Pattern Recognition, pp. 343–350 (1996)
Kanade, T., Okutomi, M.: A stereo matching algorithm with an adaptive window: Theory and experiment. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 920–932 (1994)
Frey, B.J., Jojic, N.: Learning graphical models of images, videos and their spatial transformations. In: Proceedings of the Sixteenth Conference on Uncertainty in Artifical Intelligence (2000)
Dellaert, F., Thrun, S., Thorpe, C.: Jacobian images of super-resolved texture maps for modelbased motion estimation and tracking. In: IEEE Workshop on Applications of Computer Vision, pp. 2–7 (1998)
Wang, J., Adelson, E.: Representing moving images with layers. IEEE Transactions on Image Processing, Special Issue: Image Sequence Compression 4, 625–638 (1994)
Blake, A., Isard, M.: Active Contours. Springer, Heidelberg (1998)
Isard, M., Blake, A.: Icondensation: Unifying low-level and high-level tracking in a stochastic framework. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 893–908. Springer, Heidelberg (1998)
Kristjansson, T., Frey, B.: Keeping flexible active contours on track using metropolis updates. In: Advances is Neural Information Processing (NIPS), pp. 859–865 (2000)
Cootes, T., Edwards, G., Taylor, C.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)
Murphy, K., Russell, S.: Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. In: (Sequential Monte Carlo Methods in Practice)
Doucet, A., de Freitas, N., Murphy, K., Russell, S.: Rao-blackwellised particle filtering for dynamic bayesian networks. In: Proc. of Uncertainty in AI (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kristjansson, T., Attias, H., Hershey, J. (2004). Stereo Based 3D Tracking and Scene Learning, Employing Particle Filtering within EM. In: Pajdla, T., Matas, J. (eds) Computer Vision - ECCV 2004. ECCV 2004. Lecture Notes in Computer Science, vol 3024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24673-2_44
Download citation
DOI: https://doi.org/10.1007/978-3-540-24673-2_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21981-1
Online ISBN: 978-3-540-24673-2
eBook Packages: Springer Book Archive