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Object Tracking Based on Color Space Adjustable Technology Combined with Particle Filter
PROCEEDINGS
Ming-Tsung Yeh, Yu-Xian Huang, Shi-Ming Chen, National Changhua University of Education, Taiwan ; Chih-Chung Yu, Da-Yeh University, Taiwan ; Yi-Nung Chung, Chang-Te Lin, National Changhua University of Education, Taiwan
E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Las Vegas, NV, USA ISBN 978-1-939797-05-6 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA
Abstract
This paper proposes an approach to track moving object in real time and to predict and trace the observed targets based on the particle filter. This system includes three parts. The first is the foreground segmentation applying color space transformation to separate the moving objects from the background. The second part is the processing to filter out the noise and to mark the move targets with frame. The final part is using the particle filter to trace objects. To estimate the location of next state and track the moving objects, it applies the prior and current state based on the particle filter technology. Experimental result proved this method to be accurate for real time tracking of objects. Keywords: particle filter, foreground segmentation, object track, color space transformation
Citation
Yeh, M.T., Huang, Y.X., Chen, S.M., Yu, C.C., Chung, Y.N. & Lin, C.T. (2013). Object Tracking Based on Color Space Adjustable Technology Combined with Particle Filter. In T. Bastiaens & G. Marks (Eds.), Proceedings of E-Learn 2013--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 1623-1628). Las Vegas, NV, USA: Association for the Advancement of Computing in Education (AACE). Retrieved August 14, 2024 from https://www.learntechlib.org/primary/p/115108/.
© 2013 Association for the Advancement of Computing in Education (AACE)
References
View References & Citations Map- Rouhollah Dianat and Shohreh Kasaei (2010), Change Detection in Optical Remote Sensing Images Using DifferenceBased Methods and Spatial Information, IEEE Geoscience and Remote Sensing Letters, Vol. 7, Issue 1, (pp.215 – 219).
- Vinu Thomas and Ajoy Kumar Ray (2011), Fuzzy Particle Filter for Video Surveillance, IEEE Transactions on Fuzzy Systems, Vol. 19, Issue 5, (pp. 937 – 945).
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