Abstract
In order to get a better solution on non-linear and non-Gaussian targets tracking problems, a novel multi-object tracking framework based on particle filter with data association is proposed. Firstly, a self-adaptive size tracking window algorithm is given and integrated into the tracking framework for the changes scale of moving objects, then a novel data association method based on JPDA is proposed, in which the tracking window, the observation data of the same object at adjacent frames and the different objects at the same frame are jointly associated to achieve accurate data association during tracking. The simulation results indicates that the proposed framework can be used effectively in multi-object tracking, it have the ability of dealing with the challenges such as object occlusion, separating, merging, and pot up.
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
Niu, R.X., Blum, R.S., Varshney, P.K., Drozd, A.L.: Target Localization and Tracking in Noncoherent Multiple-Input Multiple-Output Radar Systems. IEEE Transactions on Aerospace and Electronic Systems 48(2), 1466–1489 (2012)
Mahler, R.P.S.: Multi-target Bayes filtering via first-order multi-target moments. IEEE Transaction on Aerospace and Electronic Systems 39(4), 1152–1178 (2003)
Mei, X., Ling, H.: Robust Visual Tracking and Vehicle Classification via Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(11), 2259–2272 (2011)
Ling, J.-G., Liu, E.-Q., Yang, J., Yang, L.: Approach of infrared small target motion prediction and tracking based on H∞filter. J. Infrared Milim Waves 24(5), 366–369 (2005)
Bar-Shalom, Y., Fortmann, T.E.: Tracking and data association, pp. 128–152. Academic Press, San Diego (1988)
Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for bayesian filtering. Statistics and Computing 10(3), 197–208 (2000)
Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking. IEEE Transaction on Signal Processing, Special Issue on Monte Carlo Methods, 174–188 (2002)
Isard, M., MacCormick, J.: BraMBLe: a Bayesian multiple-blob tracker. In: 8th IEEE International Conference on Computer Vision, pp. 34–41. IEEE Press, New York (2001)
Smith, K., Perez, D., Odobez, J.M.: Using particles to track varying numbers of interacting people. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 962–969. IEEE Press, New York (2005)
Chan, Y.-M., Huang, S.-S., Fu, L.-C., Hsiao, P.-Y., Lo, M.-F.: Vehicle detection and tracking under various lighting conditions using a particle filter. IET Intelligent Transport Systems 6(1), 1–8 (2012)
Zhang, T., Fei S.-M., Wang, L.-L.: Modified particle filter for object tracking in low frame rate video.In: Proceedings of the 28th Chinese Control Conference, pp. 2552–2557. IEEE Press, New York (2009)
Qian, H.-M., Mao, Y.-B., Wang, Z.-Q.: Mean shift tracking with self-updating tracking window. Journal of Image and Graphics 12(2), 245–249 (2007)
Vo, B.-N., Ma, W.-K., Singh, S.: Localizing an unknown time-varying number of speakers: a bayesian random finite set approach. In: 30th International Conference on Acoustics, Speech Signal Processing, pp.1073–1076. IEEE Press, New York (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, P., Wang, Y. (2015). Object Tracking Based on Particle Filter with Data Association. In: Tan, T., Ruan, Q., Wang, S., Ma, H., Di, K. (eds) Advances in Image and Graphics Technologies. IGTA 2015. Communications in Computer and Information Science, vol 525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47791-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-662-47791-5_13
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-47790-8
Online ISBN: 978-3-662-47791-5
eBook Packages: Computer ScienceComputer Science (R0)