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500-fps face tracking system

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Abstract

In this paper, we propose a high-speed vision system that can be applied to real-time face tracking at 500 fps using GPU acceleration of a boosting-based face tracking algorithm. By assuming a small image displacement between frames, which is a property of high-frame rate vision, we develop an improved boosting-based face tracking algorithm for fast face tracking by enhancing the Viola–Jones face detector. In the improved algorithm, face detection can be efficiently accelerated by reducing the number of window searches for Haar-like features, and the tracked face pattern can be localized pixel-wise even when the window is sparsely scanned for a larger face pattern by introducing skin color extraction in the boosting-based face detector. The improved boosting-based face tracking algorithm is implemented on a GPU-based high-speed vision platform, and face tracking can be executed in real time at 500 fps for an 8-bit color image of 512 × 512 pixels. In order to verify the effectiveness of the developed face tracking system, we install it on a two-axis mechanical active vision system and perform several experiments for tracking face patterns.

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References

  1. Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 34–58 (2002)

    Article  Google Scholar 

  2. Yang, G., Huang, T.S.: Human face detection in complex background. Pattern Recogn. 27(1), 53–63 (1994)

    Article  Google Scholar 

  3. Yow, K.C., Cipolla, R.: Feature-based human face detection. Image Vis. Comput. 15(9), 713–735 (1997)

    Article  Google Scholar 

  4. Gong, S., McKenna, S., Raja, Y.: Modelling facial colour and identity with Gaussian mixtures. Pattern Recogn. 31(12), 1883–1892 (1998)

    Article  Google Scholar 

  5. Craw, I., Tock, D., Bennett, A.: Finding face features. In: Proceedings of Second European Conference on Computer Vision

  6. Lanitis, A., Taylor, C.J., Cootes, T.F.: An automatic face identification system using flexible appearance models. Image Vis. Comput. 13(5), 393–401 (1995)

    Article  Google Scholar 

  7. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  8. Sung, K.-K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 39–51 (1998)

    Article  Google Scholar 

  9. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 511–518 (2001)

  10. Wei, Y., Bing, X., Chareonsak, C.: FPGA implementation of AdaBoost algorithm for detection of face biometrics. In: Proceedings of IEEE International Workshop on Biomedical Circuits and Systems S1/6–17–20 (2004)

  11. Gao, C., Lu, S.-L.: Novel FPGA based Haar classifier face detection algorithma acceleration. In: Proceedings of International Conference Field Programmable Logic and Applications, pp 373–378 (2008)

  12. Cho, J., Benson, B., Mirzaei, S., Kastner, R.: Parallelized architecture of multiple classifiers for face detection. In: Proceedings of IEEE International Conference on Application-specific Systems, Architectures and Processors, pp 75–82 (2009)

  13. Hefenbrock, D., Oberg, J., Thanh, N.T.N., Kastner, R., Baden, S.B.: Accelerating Viola-Jones face detection to FPGA-level using GPUs. In: Proceedings of Annual International Symposium on Field-Programmable Custom Computing Machines, pp 11–18 (2010)

  14. Eklund, J.E., Svensson, C., Astrom, A.: VLSI implementation of a focal plane image processor—a realization of the near-sensor image processing concept. IEEE Trans. VLSI Syst. 4(3), 322–335 (1996)

    Article  Google Scholar 

  15. Komuro, T., Kagami, S., Ishikawa, M.: A dynamically reconfigurable SIMD processor for a vision chip. IEEE J. Solid-State Circuits 39(1), 265–268 (2004)

    Article  Google Scholar 

  16. Ishii, I., Yamamoto, K., Kubozono, M.: Higher order autocorrelation vision chip. IEEE Trans. Electron Devices 53(8), 1797–1804 (2006)

    Article  Google Scholar 

  17. Hirai, S., Zakoji, M., Masubuchi, A., Tsuboi, T.: Realtime FPGA-based vision system. J. Robotics Mech. 17(4), 401–409 (2005)

    Google Scholar 

  18. Watanabe, Y., Komuro, T., Ishikawa, M.: 955-fps real-time shape measurement of a moving/deforming object using high-speed vision for numerous-point analysis. In: Proceedings of IEEE International Conference on Robotics and Automation, pp 3192–3197 (2007)

  19. Ishii, I., Taniguchi, T., Sukenobe, R., Yamamoto, K.: Development of high-speed and real-time vision platform, H3 vision. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 3671–3678 (2009)

  20. Ishii, I., Tatebe, T., Gu, Q., Moriue, Y., Takaki, T., Tajima, K.: (2010) 2000 fps real-time vision system with high-frame-rate video recording. In: Proceedings of IEEE International Conference on Robotics and Automation, pp 1536–1541

  21. Papageorgiou, C.P., Oren, M., Poggio, T.: (1998) A general framework for object detection. In: Proceedings of IEEE International Conference on Computer Vision, pp 555–562

  22. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  23. Ishii, I., Nakabo, Y., Ishikawa, M.: Target tracking algorithm for 1 ms visual feedback system using massively parallel processing. In: Proceedings of IEEE International Conference on Robotics and Automation, pp 2309–2314 (1996)

  24. Namiki, A., Imai, Y., Ishikawa, M.: Development of a high-speed multifingered hand system and its application to catching. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 2666–2671 (2003)

  25. Senoo, T., Namiki, A., Ishikawa, M.: High-speed batting using a multi-jointed manipulator. In: Proceedings of IEEE International Conference on Robotics and Automation, pp 1191–1196 (2004)

  26. Cho, J., Benson, B., Cheamanukul, S., Kastner, R.: Increased performance of FPGA-based color classification system. In: Proceedings of IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, pp 29–32 (2010)

  27. Ishii, I., Tatebe, T., Gu, Q., Takaki, T.: Color-histogram-based tracking at 2000 fps. J. Electron. Imaging 21(1):013010 (2012)

    Google Scholar 

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Authors and Affiliations

  1. 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8527, Japan

    Idaku Ishii, Tomoki Ichida, Qingyi Gu & Takeshi Takaki

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  1. Idaku Ishii
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  2. Tomoki Ichida
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  3. Qingyi Gu
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  4. Takeshi Takaki
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Correspondence to Idaku Ishii.

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Ishii, I., Ichida, T., Gu, Q. et al. 500-fps face tracking system. J Real-Time Image Proc 8, 379–388 (2013). https://doi.org/10.1007/s11554-012-0255-8

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