Research of Tracking and Prediction of Moving Object with Kalman

Zhong Yong Wang; Song Chao Yang

doi:10.4028/www.scientific.net/AMM.599-601.1287

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 599-601Research of Tracking and Prediction of Moving...

Research of Tracking and Prediction of Moving Object with Kalman

Abstract:

This paper puts forward a complete track forecasting models, using Kalman filter to track and predict the movement of objects without prior knowledge. Use the extracted Harris corner to calculate optical flow between two frames by L-K pyramid method, getting the convex hull of moving objects by optical flow clustering to separate the moving objects from background. Tracking and predicting the gravity of moving objects convex hull can solve the occlusion and separation problem between moving objects. Computer simulation and field test results show that the proposed method has higher tracking accuracy, and small amount of calculation.

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Periodical:

Applied Mechanics and Materials (Volumes 599-601)

Pages:

1287-1290

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.599-601.1287

Citation:

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Online since:

August 2014

Authors:

Zhong Yong Wang*, Song Chao Yang

Keywords:

Cluster Analysis, Kalman, L-K Optical Flow, Predict, Track

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* - Corresponding Author

References

[1] Shin J H，Kim S J，Kang S K. Optical flow-based real-time object tracking using non prior -training active feature model, J. Real-Time Imaging. 2005(11): 204-218.

DOI: 10.1016/j.rti.2005.03.006

Google Scholar

[2] S M Smith，J M Brady. ASSERT-2: Real-time motion segmentation and shape tracking. IEEE Trans, J. On Pattern Analysis and Machine Intelligence. 1995(17): 814- 82.

DOI: 10.1109/34.400573

Google Scholar

[3] Li J Z, Yuan L, A moving target tracking method based on characteristics of optical flow detection , J. Systems Engineering and Electronics. 2005(3): 23-26.

Google Scholar

[4] O. A. Omer, Region-based Horn-Schunk Optical Flow Estimation. Electronics Communications and Computers, 2012 Japan Egypt Conference on. 2012: 73- 78.

DOI: 10.1109/jec-ecc.2012.6186960

Google Scholar

[5] B. D. Lucas, and T. Kanade, An iterative image registration technique with an application, J. Proceedings of the 1981 DARPA Imaging Understanding Workshop. 1981: 121-130.

Google Scholar

[6] R. Kalman, A new approach to linear filtering and prediction problems, J. Journal of Basic Engineering. 1960(82): 35 - 45.

DOI: 10.1115/1.3662552

Google Scholar

[7] C. Harris and M. Stephens. A combined corner and edge detector. Proceedings of the 4th Alvey Vision Conference. 1988: 147 – 151.

DOI: 10.5244/c.2.23

Google Scholar

[8] J. Shi and C. Tomasi. Good features to track. 9th IEEE Conference on Computer Vision and Pattern Recognition. 1994: 15-19.

DOI: 10.1109/cvpr.1994.323794

Google Scholar