Journal of Real-Time Image Processing

, Volume 11, Issue 2, pp 251–269 | Cite as

High frame-rate tracking of multiple color-patterned objects

  • Qingyi Gu
  • Tadayoshi Aoyama
  • Takeshi Takaki
  • Idaku Ishii
Special Issue Paper

Abstract

In this study, we develop a high frame-rate vision system that can execute color histogram-based tracking of multiple color-patterned objects in a 512 × 512 image at 2,000 fps by implementing an expanded cell-based labeling algorithm as the hardware logic. In the hardware implementation of the expanded cell-based labeling algorithm, the 16-bin hue-based color histograms of 1,024 color-patterned objects in an image can be extracted simultaneously by dividing the image into 8 × 8 cells concurrently, after calculating the 0th, 1st, and 2nd moment features to obtain the positions, areas, and orientation angles of multiple objects. We verified the effectiveness of our developed tracking system by performing several experiments using multiple color-patterned objects, which were always tracked even when they moved rapidly with occlusions in the camera views.

Keywords

Hardware implementation High-frame-rate vision  Multiple color-patterned objects tracking Cell-based labeling 

References

  1. 1.
    Swain, M.J., Ballard, D.H.: Color indexing. Int. J. Comput. Vis 7(1), 11–32 (1991)CrossRefGoogle Scholar
  2. 2.
    Funt, B.V., Finlayson, G.D.: Color constant color indexing. IEEE Trans. Patt. Mach. Anal. Intell 17(5), 522–529 (1995)CrossRefGoogle Scholar
  3. 3.
    Bradski, G.R.: Computer vision face tracking for use in a perceptual user interface. In: Proc. IEEE Workshop Appli. Comput. Vis. pp. 214–219 (1998)Google Scholar
  4. 4.
    Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean-shift. In: Proceedings of IEEE international conference on computer vision pattern recognition, vol. 2, pp. 142–149 (2000)Google Scholar
  5. 5.
    Leichter, I., Lindenbaum, M., Rivlin, E.: Mean Shift tracking with multiple reference color histograms. Comput. Vis. Image Underst. 114, 400–408 (2010)CrossRefGoogle Scholar
  6. 6.
    Koga, T., Ochiai, K., Suetake, N., Uchino, E.: Efficiency analysis of Preset Color Histogram in Mean-shift-based Crow Tracking. In: Proceedings of the international multiconference of engineering and computer scientists 2012 (IMECS2012), vol. I, pp. 736–739 (2010)Google Scholar
  7. 7.
    Hayashi, Y., Fujiyoshi, H.: Mean-shift-based color tracking in illuminance change. In: RoboCup 2007: Robot Soccer World Cup XI., pp. 302–311 (2007)Google Scholar
  8. 8.
    Jeong, D., Yang, Y.K., Kang, D.G., Ra, J.B.: Real-time head tracking based on color and shape information. In: Proceedings of the SPIE image and video communications and processing 2005, vol. 5685, pp. 912–923 (2005)Google Scholar
  9. 9.
    Isard, M., Blake, A.: CONDENSATION—conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)CrossRefGoogle Scholar
  10. 10.
    Nummiaro, K., Koller-Meier, E., Van Gool, L.: An adaptive color-based particle filter. Image Vis. Comput. 21(1), 99–110 (2003)Google Scholar
  11. 11.
    Chai, Y.J., Hwang, J.S., Chang, K., Choi, Y.J., Kim, T.Y.: Realtime User Interface using Particle Filter with Integral Histogram. In: International conference on consumer electronics (ICCE), pp. 245–246 (2010)Google Scholar
  12. 12.
    Zhao, Q., Tao, H.: A motion observable representation using color correlogram and its applications to tracking. Comput. Vis. Image Underst. 113(2), 273–290 (2009)CrossRefGoogle Scholar
  13. 13.
    Kalarot, R., Morris, J.: Real-time correlogram tracking for airborne traffic surveillance. In: International conference image and vision computing New Zealand, pp. 18–22 (2009)Google Scholar
  14. 14.
    Kotoulas, L., Andreadis, I.: Colour histogram content-based image retrieval and hardware implementation. In: International conference image and vision computing New Zealand, vol. 150, pp. 387–393 (2003)Google Scholar
  15. 15.
    Fleck, S., Lanwer, S., Straser, W.: A smart camera approach to real-time tracking. In: 13th European Signal Processing Conference (EUSIPCO), 4–8 Sept 2005Google Scholar
  16. 16.
    Wang, T.H., Chang, J.Y., Chen, L.G.: Algorithm and architecture for object tracking using particle filter. In: IEEE international conference on multimedia and expo, 2009 (ICME 2009), pp. 1374–1377 (2009)Google Scholar
  17. 17.
    Cherng, D.C., Yang, S.Y., Shen, C.F., Lu, Y.C.: Real time color based particle filtering for object tracking with dual cache architecture. In: 8th IEEE international conference on advanced video and signal-based surveillance (AVSS), pp. 148–153 (2011)Google Scholar
  18. 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)Google Scholar
  19. 19.
    Hirai, S., Zakoji, M., Masubuchi, A., Tsuboi, T.: Realtime FPGA-based vision system. J. Robot. Mechatron. 17(4), 401–409 (2005)Google Scholar
  20. 20.
    Ishii, I., Sukenobe, R., Taniguchi, T., Yamamoto, K.: Development of high-speed and real-time vision platform, H3 Vision. In: Proceedings of IEEE/RSJ international conference on intelligent robots systems, pp. 3671–3678 (2009)Google Scholar
  21. 21.
    Ishii, I. et al.: 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 (2010)Google Scholar
  22. 22.
    Cho, J., Benson, B., Mirazaei, S., Kastner, R.: Parallelized architecture of multiple classifiers for face detection. In: Proceedings of 20th IEEE international conference on application-specific systems, architecture and processors, 2009 Proceedings: Boston, MA, July 7, pp. 75–82 (2009)Google Scholar
  23. 23.
    Ishii, I., Tatebe, T., Gu, Q., Takaki, T.: Color-histogram-based tracking at 2000 fps. J. Electron. Imaging 21(1) (2012)Google Scholar
  24. 24.
    Gu, Q., Takaki, T., Ishii, I.: A fast multi-object extraction algorithm based on cell-based connected components labeling. In: IEICE Trans. Inf. Syst. E95-D(2), pp. 636–645 (2012) Google Scholar
  25. 25.
    Gu, Q., Takaki, T., Ishii, I.: 2000-fps multi-object extraction based on cell-based labeling. In: Proceedings of IEEE international conference on image processing, pp. 3761–3764 (2010)Google Scholar
  26. 26.
    Gu, Q., Takaki, T., Ishii, I.: Fast FPGA-based multi-object feature extraction. IEEE Trans. Circuits Syst. Video Technol. 23(1), 30–45 (2013)CrossRefGoogle Scholar
  27. 27.
    Smith, A.R.: Color gamut tranform pairs. Proc. SIGGRAPH Comput. Graph 12(3), 12–19 (1978)CrossRefGoogle Scholar
  28. 28.
    Ma, N., Bailey, D., Johnston, C.: (2008) Optimised single pass connected components analysis. In: International conference on field programmable technology, pp. 185–192.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Qingyi Gu
    • 1
  • Tadayoshi Aoyama
    • 1
  • Takeshi Takaki
    • 1
  • Idaku Ishii
    • 1
  1. 1.Hiroshima UniversityHigashi-HiroshimaJapan

Personalised recommendations