Abstract
This paper presents a model OMDB for mining the region information of non-rigid foreground object in video flow with dynamic background. The model constructs RDM algorithm and optimize the strategy of region matching using Q-learning to obtain better motion information of regions. Moreover, OMDB utilizes NEA algorithm to detect and merge gradually object regions of foreground based on the characteristics that there is motion difference between foreground and background and the regions of an object maintain integrality during moving. Experimental results on extracting region information of foreground object and tracking the object are presented to demonstrate the efficacy of the proposed model.
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
Magee, D.R.: Tracking multiple vehicles using foreground, background and motion models. Image and Vision Computing 22, 143–155 (2004)
Sclaroff, S., Isidoro, J.: Active blobs: region-based, deformable appearance models. Computer Vision and Image Understanding 89, 197–225 (2003)
Rosales, R., Sclaroff, S.: A framework for heading-guided recognition of human activity. Computer Vision and Image Understanding 91, 335–367 (2003)
Patras, I., Hendriks, E.A., Lagendijk, R.L.: Semi-automatic object-based video segmentation with labeling of color segments. Signal Processing: Image Communication 18, 51–65 (2003)
Grau, V., Mariano, A.R.: Hierarchical image segmentation using a correspondence with a tree model. Pattern Recognition 37, 47–59 (2004)
Zeng, C., Cao, J.H., Peng, Z.Y.: A novel 3D video trajectory tracking method. In: The Fourth International Conference on Computer and Information Technology, pp. 221–226 (2004)
Kok, J.R., Vlassis, N.: Sparse tabular multiagent Q-learning. In: Proceedings of the Annual Machine Learning Conference of Belgium and The Netherlands, pp. 65–71 (2004)
Doretto, G., Chiuso, A.: Dynamic Textures. International Journal of Computer Vision 51(2), 91–109 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zeng, C., Cao, J., Fang, Y., Du, P. (2005). Data Mining Based on Objects in Video Flow with Dynamic Background. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_46
Download citation
DOI: https://doi.org/10.1007/11527503_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
Online ISBN: 978-3-540-31877-4
eBook Packages: Computer ScienceComputer Science (R0)