Research on Application of Camshift and Kalman Filter Algorithm in Video Object Tracking

Cheng Yi Guo; Wen Bing Fan

doi:10.4028/www.scientific.net/AMR.1049-1050.1685

Paper Titles

Recurrent Wavelet Neural Network Based on Immune Evolving Algorithm and its Application in Nonlinear Modeling
p.1666

An Improve Firefly Algorithm and its Application in Nonlinear Equation Groups
p.1670

MSRM Based Object Extraction Method for Image Sequences
p.1675

Logistics Distribution Route Optimization Based on Chaotic Cuckoo Algorithm
p.1681

Research on Application of Camshift and Kalman Filter Algorithm in Video Object Tracking
p.1685

The Shortest Path Optimization Based on Mutation Particle Swarm Optimization Algorithm
p.1690

Non-Stationary Signal Analysis Based on Hilbert-Huang Transform and Median Filter
p.1694

Research on Blurred Image Restoration Quality Evaluation Method
p.1698

An Improved AdaBoost Algorithm
p.1703

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1049-1050Research on Application of Camshift and Kalman...

Research on Application of Camshift and Kalman Filter Algorithm in Video Object Tracking

Abstract:

When the background is complex and there is a lot color similar pixel interference, it may lead to location and size of Camshift algorithm’s search window abnormal so as to tracking failure. Aiming at these problems, this paper proposed a algorithm that combinating Camshift algorithm and Kalman filter. Kalman filter can predict the position of the moving object. Camshift algorithm adjusted the position and size of search window by using the prediction, so as to ensure the correct operation of the Camshift algorithm. Experimental results show that the proposed algorithm can effectively overcome the large area of similar color background and occlusion and many colors similar moving targets interference and other issues, improve the accuracy and robustness of target tracking algorithm.

You might also be interested in these eBooks

Modern Technologies in Materials, Mechanics and Intelligent Systems

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 1049-1050)

Pages:

1685-1689

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1049-1050.1685

Citation:

Cite this paper

Online since:

October 2014

Authors:

Cheng Yi Guo*, Wen Bing Fan

Keywords:

Continuously Adaptive, Kalman Filter, Mean Shift, Object Tracking, Search Window

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] J. Liang,J. Xiang J.H. Hou. An effective automatic tracking algorithm based on Camshift and Kalman filter: Microcomputer & Its Applications, Vol: 30(2011)No. 24, pp.28-31.

Google Scholar

[2] L.J. Wu. The multiple target tracking algorithm based on Camshift and Kalman filtering(MS, South-Central University for Nationalities, China 2010), p.4.

Google Scholar

[3] J. Li,J.L. Fan. Algorithm for moving object tracking based on Kalman filter: Application Research of Computers. Vol: 27(2010)No. 8, pp.3162-3164.

Google Scholar

[4] J.Y. Zuo,Y. Liang,Q. Pan C.H. Zhao H.C. Zhang. Camshift Tracker Based on Multiple Color Distribution Models: ACTA AUTOMATICA SINICA, Vol: 34(2008)No. 7, pp.736-742.

DOI: 10.3724/sp.j.1004.2008.00736

Google Scholar

[5] Tsung-Han Tsai, Guan-Jun Chen, Wen-Liang Tzeng A Novel Foreground/Background Decision using Unsupervsised Segmentation of Moving Objects in Video Sequences. Circuits and Systems, Vol: 3(2003), pp.1587-1590.

DOI: 10.1109/mwscas.2003.1562386

Google Scholar

[6] MOSCHENI F, BHATTACHARJEE S, KUNT M. Spatiotemporal segmentation based on region merging. IEEE Trans on Pattern Analysis and Machine Intelligence, Vol: 20(1998) No. 3, pp.879-915.

DOI: 10.1109/34.713358

Google Scholar

[7] Robert Laganiere. OpenCV 2 Computer Vision Application Programming Cookbook(Science Press, China 2013).

Google Scholar