Abstract
This paper proposes a novel particle filtering framework for multi-target tracking by using online learned class-specific and instance-specific cues, called Data-Driven Particle Filtering (DDPF). The learned cues include an online learned geometrical model for excluding detection outliers that violate geometrical constraints, global pose estimators shared by all targets for particle refinement, and online Boosting based appearance models which select discriminative features to distinguish different individuals. Targets are clustered into two categories. Separated-target is tracked by an ISPF (incremental self-tuning particle filtering) tracker, in which particles are incrementally drawn and tuned to their best states by a learned global pose estimator; target-group is tracked by a joint-state particle filtering method in which occlusion reasoning is conducted. Experimental results on challenging datasets show the effectiveness and efficiency of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Reid, D.: An algorithm for tracking multiple targets. IEEE Trans. Automatic Control 24, 84–90 (1979)
Bar-Shalom, Y., Fortmann, T., Scheffe, M.: Joint probabilistic data association for multiple targets in clutter. In: Proc. Conf. Information Sciences and Systems (1980)
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. IEEE Trans. on PAMI 23, 560–576 (2001)
Khan, Z., Balch, T., Dellaert, F.: Multitarget tracking with split and merged measurements. In: Proc. of CVPR 2005 (2005)
Isard, M., MacCormick, J.: Bramble: A bayesian multipleblob tracker. In: Proc. of ICCV 2001 (2001)
Okuma, K., Taleghani, A., de Freitas, N., Little, J.J., Lowe, D.G.: A boosted particle filter: Multitarget detection and tracking. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 28–39. Springer, Heidelberg (2004)
Vermaak, J., Doucet, A., Perez, P.: Maintaining multi-modality through mixture tracking. In: Proc. of ICCV 2003 (2003)
Yu, T., Wu, Y.: Decentralized multiple target tracking using netted collaborative autonomous trackers. In: Proc. of CVPR 2005 (2005)
Qu, W., Schonfeld, D., Mohamed, M.: Real-time interactively distributed multi-object tracking using a magnetic- inertia potential model. In: Proc. of ICCV 2005 (2005)
Khan, Z., Balch, T., Dellaert, F.: Mcmc-based particle filtering for tracking a variable number of interacting targets. PAMI 27, 1805–1819 (2005)
Yang, M., Yu, T., Wu, Y.: Game-theoretic multiple target tracking. In: Proc. of ICCV 2007 (2007)
Zhang, X., Hu, W., Li, W., Qu, W., Maybank, S.: Multi-object tracking via species based particle swarm optimization. In: ICCV 2009 WS on Visual Surveillance (2009)
Hess, R., Fern, A.: Discriminatively trained particle filters for complex multi-object tracking. In: Proc. of CVPR 2009 (2009)
Breitenstein, M., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.: Robust tracking-by-detection using a detector confidence particle filter. In: ICCV 2009 (2009)
Li, M., Chen, W., Huang, K., Tan, T.: Visual tracking via incremental self-tuning particle filtering on the affine group. In: Proc. of CVPR 2010 (2010)
Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. Journal of IEEE Trans. on Signal Processing 50, 174–188 (2002)
Vijayakumar, S., Souza, A.D., Schaal, S.: Incremental online learning in high dimensions. Neural Computation 17, 2602–2634 (2005)
Li, M., Zhang, Z., Huang, K., Tan, T.: Pyramidal statistics of oriented filtering for robust pedestrian detection. In: Proc. of ICCV 2009 WS on Visual Surveillance (2009)
Grabner, H., Bischof, H.: On-line boosting and vision. In: Proc. of CVPR 2006 (2006)
Dalal, N., Triggls, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
Hastings, W.: Monte carlo sampling methods using markov chains and their applications. Biometrika 57, 97–109 (1970)
PETS09dataset, http://www.cvg.rdg.ac.uk/PETS2009/a.html
PETS04dataset, http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, M., Chen, W., Huang, K., Tan, T. (2011). Multi-Target Tracking by Learning Class-Specific and Instance-Specific Cues. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19309-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-19309-5_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19308-8
Online ISBN: 978-3-642-19309-5
eBook Packages: Computer ScienceComputer Science (R0)