Abstract
In this paper we propose a method which can improve MRF-Particle filters used to solve the hijacking problem; independent particle filters for tracking each object can be kidnapped by a neighboring target which has higher likelihood than that of real target. In the method the motion model built by Markov random field (MRF) has been usefully applied for avoiding hijacking by lowering the weight of particles which are very close to any neighboring target. The MRF unary and pairwise potential functions of neighboring targets are defined as the penalty function to lower the particle’s weights. And potential function can be reused for defining action likelihood which can measure the motion of object group.
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. 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)
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 Int. Conf. on Robotics and Automation (2001)
Khan, Z., Balch, T., Dellaert, F.: MCMC-based Particle Filtering for Tracking a Variable Number of Interacting Targets. PAMI 27(11), 1805–1819 (2005)
Viola, P., Jones, M.: Robust Real-time Object Detection. Int. Journal of Computer Vision 57(2), 137–154 (2002)
Islam, M.Z., Oh, C.M., Lee, J.S., Lee, C.W.: Multi-Part Histogram based Visual Tracking with Maximum of Posteriori. In: Int. Conf. on Computer Engineering and Technology (2010)
Stalder, S., Grabner, H., Van Gool, L.: Cascaded confidence filtering for improved tracking-by-detection. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 369–382. Springer, Heidelberg (2010)
Han, J.Y.: Low-cost multi-touch sensing through frustrated total internal reflection. In: Proc. 18th Annu. Assoc. Comput. Machinery Symp. User Interface Software Technology, pp. 115–118 (2005)
Oh, C.M., Lee, Y.C., Lee, C.W.: MRF-based Particle Filters for Multi-touch Tracking and Gesture Likelihoods. In: 11th IEEE International Conference on Computer and Information Technology, pp. 144–149 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Oh, CM., Lee, CW. (2013). Tracking Multiple Objects and Action Likelihoods. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Studies in Computational Intelligence, vol 492. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00738-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-00738-0_17
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00737-3
Online ISBN: 978-3-319-00738-0
eBook Packages: EngineeringEngineering (R0)