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Target tracking algorithm based on optical flow method using corner detection

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Abstract

The tracking speed and accuracy are two most important parameters for a target tracking system. In our study, the proposed target tracking algorithm combines the Harris method and the optical flow method. To improve the tracking speed, the Harris method is initially used to extract some target corner features, and the optical flow method is then used to more accurately match corner features for the subsequent video frames. When the tracked target is rotated or distorted, the barycenter algorithm is employed to compute the barycenter of those matched features of target. To meet the real-time-tracking requirement, a small-zone image searching method and a high speed digital signal processing system are also designed. Our experimental study shows that the method described in this paper has high accuracy of target tracking, and can be applied to the situations of rotated, distorted, and/or shielded targets, although it has a limitation that it is only suitable for smaller targets.

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References

  1. Barron J, Klette R (2002) Quantitative color optical flow. Proc 16th Int Conf Pattern Recognit 4:251–255

    Google Scholar 

  2. Chen L (2005) A survey of corner detection algorithms. Techniques of Automation and Applications 24:5

    Google Scholar 

  3. Comaniciu D, Ramesh V, Meer P (2000) Real-time tracking of non-rigid objects using mean shift. Proc IEEE Conf Comput Vis Pattern Recogn 2:142–149

    Google Scholar 

  4. Enkelman W (1998) Investigations of multigrid algorithms for the estimation of optic f1ow fields in images sequences. Comput Vis Graph Image Process 43:150–177

    Article  Google Scholar 

  5. Fermuller C, Shulman D, Aloimonos Y (2001) The statistics of optical flow. Comput Vis Image Underst 82:1–32

    Article  Google Scholar 

  6. Fusiello A, Trucco E, Tommasini T, Roberto V (1999) Improving feature tracking with robust statistics. Pattern Anal Applicat 2:312–320

    Article  Google Scholar 

  7. Georgescu B, Meer P (2004) Point matching under large image deformations and illumination changes. IEEE Trans Pattern Anal Mach Intell 26(6):674–688

    Article  Google Scholar 

  8. Griffin A, Kittler J (2002) An active mesh based tracker for improved feature correspondences. Pattern Recogn lett 23:443–449

    Article  MATH  Google Scholar 

  9. Harris C, Stephens M (1988) A combined corner and edge detector. Proc Alvey Vis Conf 15:147–151

    Google Scholar 

  10. Lucas B, Kanade T (1981) An iterative image registration technique with application to stereo vision. Proc Int Joint Conf Artif Intell 674–679, Aug. 1981

  11. Shi J, Tomasi C (1994) Good features to track. Proc 1994 IEEE Conf Comput Vis Pattern Recogn 593–600, June 1994

  12. Song H, Zhu M, Hu S (2004) The real-time target track process system design and the fast arithmetic research. Proceedings of SPIE on Passive Components and Fiber-Based Devices, Vol 5623, Beijing, China, pp 274–283, November 9–11, 2004

  13. Texas Instruments (2005) TMS320C6414T, TMS320C6415T, and TMS320C6416T FIXED-POINT DIGITAL SIGNAL PROCESSOR, SPRS226H, 2005

  14. Tommasini T, Fusiello A, Trucco E, Roberto V (1998) Making good features track better. Proc 1998 IEEE Conf Comput Vis Pattern Recogn 178–183, June 1998

Download references

Acknowledgements

This work is supported by The National Natural Science Foundation of China (No. 60873163).

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Correspondence to Hua-jun Song.

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Song, Hj., Shen, Ml. Target tracking algorithm based on optical flow method using corner detection. Multimed Tools Appl 52, 121–131 (2011). https://doi.org/10.1007/s11042-010-0464-8

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