Abstract
This paper presents a novel probabilistic approach to integrating multiple cues in visual tracking. We perform tracking in different cues by interacting processes. Each process is represented by a Hidden Markov Model, and these parallel processes are arranged in a chain topology. The resulting Linked Hidden Markov Models naturally allow the use of particle filters and Belief Propagation in a unified framework. In particular, a target is tracked in each cue by a particle filter, and the particle filters in different cues interact via a message passing scheme. The general framework of our approach allows a customized combination of different cues in different situations, which is desirable from the implementation point of view. Our examples selectively integrate four visual cues including color, edges, motion and contours. We demonstrate empirically that the ordering of the cues is nearly inconsequential, and that our approach is superior to other approaches such as Independent Integration and Hierarchical Integration in terms of flexibility and robustness.
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
Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: European Conference on Computer Vision, Copenhagen, Denmark, vol. 1, pp. 661–675 (2002)
Taylor, G., Kleeman, L.: Fusion of multimodal visual cues for model-based object tracking. In: acra (2003)
Özuysal, M., Lepetit, V., Fleuret, F., Fua, P.: Feature harvesting for tracking-by-detection. In: ECCV (2006)
Yang, C., Duraiswami, R., Davis, L.: Fast multiple object tracking via a hierarchical particle filter. In: ICCV, Beijing, China (2005)
Pérez, P., Vermaak, J., Blake, A.: Data fusion for visual tracking with particles. Proceedings of the IEEE 92(3), 495–513 (2004)
Isard, M., Blake, A.: Condensation – conditional density propagation for visual tracking. International Journal of Computer Vision 29(2), 5–28 (1998)
Birchfield, S.: Elliptical head tracking using intensity gradients and color histograms. In: CVPR, Santa Barbara, CA, pp. 232–237 (1998)
Triesch, J., Malsburg, C.v.: Self-organized integration of adaptive visual cues for face tracking. In: The Fourth IEEE International Conference on Automatic Face and Gesture Recognition (2000)
Giebell, J., Gavrila1, D., Schnörr, C.: A bayesian framework for multi-cue 3d object tracking. In: ECCV, Prague, Czech Republic (2004)
Leichter, I., Lindenbaum, M., Rivlin, E.: A general framework for combining visual trackers: The black boxes approach. IJCV 67(3), 343–363 (2006)
Wu, Y., Huang, T.: Robust visual tracking by integrating multiple cues based on co-inference learning. International Journal of Computer Vision 58(1), 55–71 (2004)
Brand, M.: Coupled hidden Markov models for modeling interacting processes. Technical report, MIT Media Lab Perceptual Computing (1997)
Brasnett, P., Mihaylova, L., Canagarajah, N., Bull, D.: Particle filtering with multiple cues for object tracking in video sequences. In: Proceeding of SPIE-Image and Video Communications, vol. 5685, pp. 430–441 (2005)
Wang, H., Suter, D.: Efficient visual tracking by probabilistic fusion of multiple cues. In: ICPR, HongKong (2006)
Spengler, M., Schiele, B.: Towards robust multi-cue integration for visual tracking. In: MVA, vol. 14, pp. 50–58 (2003)
Gavrila, D.M., Munder, S.: Multi-cue pedestrian detection and tracking from a moving vehicle. International Journal of Computer Vision (2007)
Sudderth, E., Ihler, A., Freeman, W., Willsky, A.: Nonparametric belief propagation. In: IEEE Conference on Computer Vision and Pattern Recognition, Madison, WI, vol. 2, pp. 605–612 (2003)
Isard, M.: Pampas: Real-valued graphical models for computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition, Madison, WI, vol. 1, pp. 613–620 (2003)
Hua, G., Wu, Y.: Multi-scale visual tracking by sequential belief propagation. In: IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, vol. 1, pp. 826–833 (2004)
Briers, M., Doucet, A., Singh, S.: Sequential auxiliary particle belief propagation. In: The Eighth International Conference on Information Fusion (2005)
Sun, J., Zheng, N., Harry, S.: Stereo matching using belief propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(7), 787–800 (2003)
Birchfield, S.T., Rangarajan, S.: Spatiograms versus histograms for region-based tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA (2005)
Porkili, F.: Integral histogram: A fast way to extract histograms in cartesian spaces. In: CVPR, San Diego, CA (2005)
Wang, H., Suter, D., Schindler, K.: Effective appearance model and similarity measure for particle filtering and visual tracking. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 606–618. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Du, W., Piater, J. (2008). A Probabilistic Approach to Integrating Multiple Cues in Visual Tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5303. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88688-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-88688-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88685-3
Online ISBN: 978-3-540-88688-4
eBook Packages: Computer ScienceComputer Science (R0)