Abstract
We consider the problem of reliably tracking multiple objects in video, such as people moving through a shopping mall or airport. In order to mitigate difficulties arising as a result of object occlusions, mergers and changes in appearance, we adopt an integrative approach in which multiple cues are exploited. Object tracking is formulated as a Bayesian parameter estimation problem. The object model used in computing the likelihood function is incrementally updated. Key to the approach is the use of a background subtraction process to deliver foreground segmentations. This enables the object colour model to be constructed using weights derived from a distance transform operating over foreground regions. Results from foreground segmentation are also used to gain improved localisation of the object within a particle filter framework. We demonstrate the effectiveness of the approach by tracking multiple objects through videos obtained from the CAVIAR dataset.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Dorin, C., Visvanathan, R., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5), 564–577 (2003)
Isard, M., Blake, A.: Condensation – conditional density propagation for visual tracking. International Journal of Computer Vision 29(1), 5–28 (1998)
Nummiaro, K., Koller-Meier, E., Gool, L.J.V.: Object tracking with an adaptive color-based particle filter. In: Proceedings of the 24th DAGM Symposium on Pattern Recognition, pp. 353–360. Springer, Heidelberg (2002)
Zhou, S.K., Chellappa, R., Moghaddam, B.: Visual tracking and recognition using appearance adaptive models in particle filters. IEEE Transactions on Image Processing 13(11), 1434–1456 (2004)
Ross, D., Lim, J., Yang, M.H.: Adaptive probabilistic visual tracking with incremental subspace update. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3022, pp. 470–482. Springer, Heidelberg (2004)
Brasnett, P.A., Mihaylova, L., Canagarajah, N., Bull, D.: Particle filtering with multiple cues for object tracking in video sequences. In: Proceedings of SPIE. Image and Video Communications and Processing, vol. 5685, pp. 430–441 (2005)
Wu, Y., Huang, T.S.: Robust visual tracking by integrating multiple cues based on co-inference learning. Int. J. Comput. Vision 58(1), 55–71 (2004)
Spengler, M., Schiele, B.: Towards robust multi-cue integration for visual tracking. Machine Vision and Applications 14, 50–58 (2003)
Perez, P., Vermaak, J., Blake, A.: Data fusion for visual tracking with particles. Proceedings of the IEEE 92(3), 495–513 (2004)
Shao, J., Zhou, S.K., Chellappa, R.: Tracking algorithm using background foreground motion models and multiple cues. In: ICASSP apos 2005. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 233–236 (2005)
Collins, R.T., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1631–1643 (2005)
Han, B., Davis, L.: Object tracking by adaptive feature extraction. In: ICIP 2004. International Conference on Image Processing, vol. 3, pp. 1501–1504 (2004)
Arulampalam, S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for on-line non-linear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing 50(2), 174–188 (2002)
Kang, J., Cohen, I., Medioni, G., Yuan, C.: Detection and tracking of moving objects from a moving platform in presence of strong parallax. In: Proceedings of the Tenth International Conference on Computer Vision, Beijing, China, vol. 1, pp. 10–17 (2005)
Kumar, P., Ranganath, S., Huang, W.: Queue based fast background modelling and fast hysteresis thresholding for better foreground segmentation. In: The Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint Conference of the Fourth International Conference on Information, Communications and Signal Processing, vol. 2, pp. 743–747 (2003)
Kumar, P., Ranganath, S., Sengupta, K., Huang, W.: Cooperative multitarget tracking with efficient split and merge handling. IEEE Transactions on Circuts and Systems for Video Technology 16(12), 1477–1490 (2006)
Jain, A.K.: Fundamentals of Digital Image Processing. Prentice Hall International, Englewood Cliffs (1989)
Rosenfeld, A., Pfaltz, J.: Distance functions in digital pictures. Pattern Recognition 1, 33–61 (1968)
Dorin, C., Visvanathan, R., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island, SC, USA, vol. 2, pp. 142–149 (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kumar, P., Brooks, M.J., Dick, A. (2007). Adaptive Multiple Object Tracking Using Colour and Segmentation Cues. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76386-4_81
Download citation
DOI: https://doi.org/10.1007/978-3-540-76386-4_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76385-7
Online ISBN: 978-3-540-76386-4
eBook Packages: Computer ScienceComputer Science (R0)