Skip to main content
SpringerLink
Log in

Directional Prediction CamShift algorithm based on Adaptive Search Pattern for moving object tracking

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Moving object tracking is a fundamental task on smart video surveillance systems, because it provides a focus of attention for further investigation. Continuously Adaptive MeanShift (CamShift) algorithm is an adaptation of the MeanShift algorithm for moving objects tracking significantly, and it has been attracting increasing interests in recent years. In this work, a new CamShift approach, Directional Prediction CamShift (DP-CamShift) algorithm, is proposed to improve the tracking accuracy rate. According to the characteristic of the center-based motion vector distribution for the real-world video sequence, this work employs an Adaptive Search Pattern (ASP) to refine the central area search. The proposed approach is more robust because it adapts the optimal search pattern methods for the most adequate direction of the moving target. Since the fast Motion Estimation (ME) method has its own moving direction feature, we can adaptively use the most proper fast ME method to the certain moving object to have the best performance. Furthermore for estimation in large motion situations, the strategy of the DP-CamShift can preserve good performance. For the test video sequences with frame size of 320 × 240, the experimental results indicate that the proposed algorithm can have an accuracy rate of 99 % and achieve 23 frames per second (FPS) processing speed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 34(3), 334–352 (2004)

    Article  Google Scholar 

  2. Wang, F., Lin, Y.: Improving particle filter with a new sampling strategy. In: International Conference on Computer Science and Education, pp. 408–412 (2009)

  3. Nouar, O.D., Ali, G., Raphael, C.: Improved object tracking with CamShift algorithm. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. II-657–II-660 (2006)

  4. Chu, H., Ye, S., Guo, Q., Liu, X.: Object tracking algorithm based on CamShift algorithm combinating with difference in frame. In: IEEE International Conference on Automation and Logistics, pp. 51–55 (2007)

  5. Zhang, C., Qiao, Y.: An improved CamShift algorithm for target tracking in video surveillance. In: IT&T Conference (2009)

  6. Wang, X., Li, X.: The study of moving target tracking based on Kalman-CamShift in the video. In: International Conference on Information Science and Engineering, pp. 1–4 (2010)

  7. Huang, S., Hong, J.: Moving object tracking system based on Camshift and Kalman filter. In: International Conference on Consumer Electronics, Communications and Networks, pp. 1423–1426 (2011)

  8. Yang, B., Zhou, H., Wang, X.: Target tracking using predicted Camshift. In: World Congress on Intelligent Control and Automation, pp. 8501–8505 (2008)

  9. Li, Y., Shen, X., Bei, S.: Real-time tracking method for moving target based on an improved Camshift algorithm. In: International Conference on Mechatronic Science, Electric Engineering and Computer, pp. 978–981 (2011)

  10. Hu, M.-C., Chang, M.-H., Wu, J.-L., Chi, L.: Robust camera calibration and player tracking in broadcast basketball video. IEEE Trans. Multimed. 13(2), 266–279 (2011)

    Article  Google Scholar 

  11. Jia, J., Chai, Y.-M., Zhao, R.-C.: Tracking of objects in image sequences using multiple degrees of freedom mean shift algorithm. J. Image Graph. 5(11), 707–713 (2006)

    Google Scholar 

  12. Chen, H., Li, Y.: Dynamic view planning by effective particles for three-dimensional tracking. IEEE Trans. Syst. Man Cybern. B Cybern. 39(1), 242–253 (2009)

    Article  Google Scholar 

  13. Li, R., Zeng, B., Liou, M.-L.: A new three-step search algorithm for block motion estimation. IEEE Trans. Circuits Syst. Video Technol. 4(4), 438–442 (1994)

    Article  Google Scholar 

  14. Zhu, S., Ma, K.-K.: A new diamond search algorithm for fast block matching motion estimation. IEEE Trans. Image Process. 9(2), 287–290 (2000)

    Article  MathSciNet  Google Scholar 

  15. Chen, T.-H., Li, Y.-F.: A novel flatted hexagon search pattern for fast block motion estimation. In: IEEE International Conference on Image Processing, vol. 3(10), pp. 1477–1480 (2004)

  16. Zhu, C., Lin, X., Chau, L.-P.: Hexagon-based search pattern for fast block motion estimation. IEEE Trans. Circuits Syst. Video Technol. 12(5), 349–355 (2002)

    Article  Google Scholar 

  17. Bradski, G.R.: Computer vision face tracking for use in a perceptual user interface. Intel Technol. J. Q2 (1998)

  18. Djouadi, A., Snorrason, Ö., Garber, F.D.: The quality of training-sample estimates of the Bhattacharyya coefficient. IEEE Trans. Pattern Anal. Mach. Intel. 12(1), 92–97 (1990)

    Article  Google Scholar 

  19. Smith, A.R.: Color gamut transform pairs. ACM SIGGRAPH Comp. Graph. 12(3), 12–19 (1978)

    Article  Google Scholar 

  20. Bilal, S., Akmeliawati, R., Jimoh, M., Salami, E., Shafie, A.A.: Dynamic approach for real-time skin detection. In: J. Real-Time Image Process. (2012)

  21. Meng, Y.: Agent-based reconfigurable architecture for real-time object tracking. J. Real-Time Image Process. 9(4), 339–351 (2009)

    Article  Google Scholar 

  22. Yin, M., Zhang, J., Sun, H., Gu, W.: Multi-cue-based CamShift guided particle filter tracking. Expert Syst. Appl. 38(5), 6313–6318 (2011)

    Article  Google Scholar 

  23. Available: http://amos.ee.tku.edu.tw/pattern/tracking/. Accessed 8 Aug 2011

  24. Available: http://www.cvg.rdg.ac.uk/slides/pets.html. Accessed 4 Nov 2005

  25. Arulampalam, S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for on-line non-linear/non-gaussian Bayesian tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002)

    Article  Google Scholar 

Download references

Acknowledgments

This research work was partially supported by the National Science Council of Taiwan under Grant Number NSC-100-2221-E-032-046.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Chinese Culture University, Taipei, Taiwan

    Chih-Hsien Hsia

  2. Department of Electrical Engineering, Tamkang University, New Taipei, Taiwan

    Yun-Jung Liou & Jen-Shiun Chiang

Authors
  1. Chih-Hsien Hsia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yun-Jung Liou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jen-Shiun Chiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jen-Shiun Chiang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hsia, CH., Liou, YJ. & Chiang, JS. Directional Prediction CamShift algorithm based on Adaptive Search Pattern for moving object tracking. J Real-Time Image Proc 12, 183–195 (2016). https://doi.org/10.1007/s11554-013-0382-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-013-0382-x

Keywords

Search

Navigation