The week's pick
Reseach Article
Moving Vehicle Segmentation Based on EM Algorithm and Fast Motion Estimation Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 2 |
| Year of Publication: 2010 |
| Authors: Priya Joy |
10.5120/52-156
|
Priya Joy . Moving Vehicle Segmentation Based on EM Algorithm and Fast Motion Estimation Algorithms. International Journal of Computer Applications. 1, 2 ( February 2010), 41-44. DOI=10.5120/52-156
Abstract
Moving vehicles segmentation is an interesting yet difficult problem in intelligent transportation system. Ordinary segmentation method is obstructed by some problems: the moving objects in dynamic scenes are the examples. Bayesian framework is utilized to classify the motion in the scenes to improve the robustness of the model and EM algorithm is used to estimate the parameters of the model. The direction and magnitude of the motion vectors are the inputs to the Bayesian rule and EM algorithm. In existing methods traditional Exhaustive search method is used for finding the motion vector. Computational cost of the Exhaustive search method is high. In this paper we are using some fast algorithms like Three Step Search and Four Step Search algorithms for reducing the computational cost. Experimental results on the scene,’ waving trees’, shows that the proposed model can segment the moving vehicles correctly with less computational cost. Quantitative evaluations demonstrate that the proposed method outperform the existing methods.
References
- Wei Zhang, Xiang Zhong Fang, Xiaokang Yang, “Moving vehicles segmentation based on Bayesian framework for Gaussian motion model “. Pattern Recognition Letters Volume 27, issue 9, 1 July 2006 pages 956-967
- Aroh Barjatya, 2004. “Block Matching Algorithms for Motion Estimation”. DIP 6620 Spring Final Project Paper
- Pless, Larson, Siebers, Westover, ”Evaluation of local models of dynamic backgrounds”.CVPR 2, 2003 pp. I/73 I/78.
- Friedman, N., Russell, S., 1997. Image segmentation in video sequences: A probabilistic approach. In: Proc. of 13th Conf. on Uncertainty in Artificial Intelligence, pp. 175–181.
- Gupte, S., Masoud, O., Martin, R.F.K., Papanikolopoulos, N.P., 2002. Detection and classification of vehicles. IEEE Trans. Intell. Transport. Syst. 3, 37–47.
- Ha, D.M., Lee, J.-M., Kim, Y.-D., 2004. Neural-edge-based vehicle detection and traffic parameter extraction. Image Vision Comput. 22, 899–907.
- Lee, K.-W., Kim, J., 1999. Moving object segmentation based on statistical motion model. In Electronics Letters. 35, pp. 1719–1720.
- Otsu, N., 1979. A threshold selection method from Gray-level histograms. IEEE Trans. Syst., Man, Cyber. 9 (1), 62–69. 966 W. Zhang et al. / Pattern Recognition Letters 27 (2006) 956–967
- Stauffer, C., Grimson, W.E.L., 1999. Adaptive background mixture models for real-time tracking. In CVPR 2, pp. 246–252.
Index Terms
Keywords