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A Target Tracking Algorithm Based on Improved Camshift and UKF
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science & Technology, Shanghai, China
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science & Technology, Shanghai, China
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science & Technology, Shanghai, China
- 1 School of Optical-Electrical and Computer Engineering, University of Shanghai for Science & Technology, Shanghai, China
- 2 School of Optical-Electrical and Computer Engineering, University of Shanghai for Science & Technology, Shanghai, China
- 3 School of Optical-Electrical and Computer Engineering, University of Shanghai for Science & Technology, Shanghai, China
Journal of Software Engineering and Applications·Volume 07 (2014)·Pages 1065–1073·Published 18 December 2014·DOI10.4236/jsea.2014.713094
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Abstract
A tracking algorithm based on improved Camshift and UKF is proposed in this paper to deal with the problems which exist in traditional Camshift algorithm, such as artificial orientation and tracking failure under color interference as well as object’s changed illumination occlusion. Meanwhile, in order to solve the sheltered problem, the UKF is combined with improved Camshift algorithm to predict the position of the target effectively. Experiment results show that the proposed algorithm can avoid the interference of the background color and solve the sheltered problem of the object, so that achieving a precise and timely tracking of moving objects. Also it has better robustness to color noises and occlusion when the object’s scale changes and deformation occurs.
KeywordsCamshiftUKFTracking AlgorithmSheltered ProblemInterference of the Background Color
- Yuan, X. and Wang, L.P. (2008) Moving Human Tracking Based on Meanshift Algorithm. Computer Engineering and Science, 30, 64-84.
- Comaniciu, D. and Ramesh, V. (2000) Real-Time Tracking of Non-Rigid Objects Using Mean Shift. IEEE Conference on Computer Vision and Pattern Recognition, New York, 142-149.
- Liu, Q., Tang, L.B. and Zhao, B.J. (2012) Meanshift Tracking Algorithm with Adaptive Tracking Window. System Engineering and Electronics, 34, 409-412.
- Bradski, G.R. (1998) Real Time Face Tracking as a Component of a Perceptual User Interface. Proceedings of IEEE Workshop Applications of Computer Vision, Princeton, 214-219.
- Wu, H.M. and Zheng, X.S. (2009) Efficient and Improved Camshift Tracking Algorithm. Computer Engineering and Application, 45, 178-180.
- Herbert, B., Andreas, E., Tinne, T., et al. (2006) Speeded-Up Robust Features. Computer Vision and Image Understanding, 404-417.
- Luo, J. and Gwun, O. (2009) A Comparison of SIFT, PCA-SIFY and SURF. International Journal of Image Processing, 3, 143-152.
- Ta, D.N., Chen, W.C., Gelfand, N., et al. (2009) SURFTrack: Efficient Tracking and Continuous Object Recognition Using Local Features Descriptors. Computer Vision and Pattern Recognition (CVPR09), Miami, 2937-2943.
- Su, D.Z., Wang, K., Wang, Y.L., et al. (2013) Moving Target Tracking Based on SURF Algorithm and Kalman Prediction. Journal of Naval Aeronautical Engineering Institute, 28, 379-382.
- Ding, P.H., Fan, X.N. and Liu, J.D. (2012) A Tracking Algorithm for Moving Object Based on Mixed Algorithms. Science Technology and Engineering, 12, 4188-4190.
- Kloihofer, W. and Kampel, M. (2010) Interest Point Based Tracking. IEEE International Conference on Pattern Recognition, Istanbul, 23-26 August 2010, 3549-3552.
- Dong, H.Y., Chen, S.F. and Zhu, J.Y. (2010) Research of Moving Targets Tracking Algorithm Based on Kalman Filtering. 2010 3rd International Conference on Intelligent Networks and Intelligent Systems, Shenyang, 1-3 November 2010, 20-23.
- Vaccarella, A., De Momi, E., Enquobahrie, A. and Ferrigno, G. (2013) Unscented Kalman Filter Based Sensor Fusion for Robust Optical and Electromagnetic Tracking in Surgical Navigation. IEEE Transactions on Instrumentation and Measurement, 62, 2067-2081. http://dx.doi.org/10.1109/TIM.2013.2248304
- Henke, D., Magnard, C., Frioud, M., Small, D., Meier, E. and Schaepman, M.E. (2012) Moving-Target Tracking in Single-Channel Wide-Beam SAR. IEEE Transactions on Geoscience and Remote Sensing, 50, 4735-4747. http://dx.doi.org/10.1109/TGRS.2012.2191561