Abstract
We describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or behavior of other targets. Such interactions cause problems for traditional approaches to the data association problem. In response, we developed a joint tracker that includes a more sophisticated motion model to maintain the identity of targets throughout an interaction, drastically reducing tracker failures. The paper presents two main contributions: (1) we show how a Markov random field (MRF) motion prior, built on the fly at each time step, can substantially improve tracking when targets interact, and (2) we show how this can be done efficiently using Markov chain Monte Carlo (MCMC) sampling. We prove that incorporating an MRF to model interactions is equivalent to adding an additional interaction factor to the importance weights in a joint particle filter. Since a joint particle filter suffers from exponential complexity in the number of tracked targets, we replace the traditional importance sampling step in the particle filter with an MCMC sampling step. The resulting filter deals efficiently and effectively with complicated interactions when targets approach each other. We present both qualitative and quantitative results to substantiate the claims made in the paper, including a large scale experiment on a video-sequence of over 10,000 frames in length.
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
Reid, D.: An algorithm for tracking multiple targets. IEEE Trans. on Automation and Control AC-24, 84–90 (1979)
Bar-Shalom, Y., Fortmann, T., Scheffe, M.: Joint probabilistic data association for multiple targets in clutter. In: Proc. Conf. on Information Sciences and Systems (1980)
Deriche, R., Faugeras, O.: Tracking line segments. Image and Vision Computing 8, 261–270 (1990)
Cox, I., Leonard, J.: Modeling a dynamic environment using a Bayesian multiple hypothesis approach. Artificial Intelligence 66, 311–344 (1994)
Rasmussen, C., Hager, G.: Probabilistic data association methods for tracking complex visual objects. PAMI 23, 560–576 (2001)
Schulz, D., Burgard, W., Fox, D., Cremers, A.B.: Tracking multiple moving targets with a mobile robot using particle filters and statistical data association. In: IEEE Intl. Conf. on Robotics and Automation (ICRA) (2001)
Balch, T., Khan, Z., Veloso, M.: Automatically tracking and analyzing the behavior of live insect colonies. In: Proc. Autonomous Agents 2001, Montreal (2001)
Gordon, N., Salmond, D., Smith, A.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Procedings F 140, 107–113 (1993)
Isard, M., Blake, A.: Contour tracking by stochastic propagation of conditional density. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 343–356. Springer, Heidelberg (1996)
Khan, Z., Balch, T., Dellaert, F.: Efficient particle filter-based tracking of multiple interacting targets using an MRF-based motion model. In: IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), Las Vegas (2003)
Berzuini, C., Best, N.G., Gilks, W., Larizza, C.: Dynamic conditional independence models and Markov chain Monte Carlo methods. Journal of the American Statistical Association 92, 1403–1412 (1996)
Gilks, W., Berzuini, C.: Following a moving target–Bayesian inference for dynamic Bayesian models. Journal of the Royal Statistical Society, Series B 63, 127–146 (2001)
Doucet, A., Gordon, N.J., Krishnamurthy, V.: Particle filters for state estimation of jump Markov linear systems. IEEE Transactions on Signal Processing 49 (2001)
Marthi, B., Pasula, H., Russel, S., Peres, Y.: Decayed MCMC filtering. In: Proceedings of the 18th Annual Conference on Uncertainty in AI (UAI) (2002)
Tweed, D., Calway, A.: Tracking many objects using subordinate Condensation. In: British Machine Vision Conference (BMVC) (2002)
MacCormick, J., Blake, A.: A probabilistic exclusion principle for tracking multiple objects. In: Intl. Conf. on Computer Vision (ICCV), pp. 572–578 (1999)
Isard, M., MacCormick, J.: BraMBLe: A Bayesian multiple-blob tracker. In: Intl. Conf. on Computer Vision (ICCV), pp. 34–41 (2001)
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, 174–188 (2002)
Li, S.: Markov Random Field Modeling in Computer Vision. Springer, Heidelberg (1995)
Neal, R.: Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, Dept. of Computer Science, University of Toronto (1993)
Gilks, W., Richardson, S., Spiegelhalter, D. (eds.): Markov chain Monte Carlo in practice. Chapman and Hall, Boca Raton (1996)
Doucet, A., de Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo Methods In Practice. Springer, New York (2001)
Hastings, W.: Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 97–109 (1970)
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
Khan, Z., Balch, T., Dellaert, F. (2004). An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets. 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_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-24673-2_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21981-1
Online ISBN: 978-3-540-24673-2
eBook Packages: Springer Book Archive