Skip to main content
SpringerLink
Log in

A Scale Self-Adaptive Tracking Method Based on Moment Invariants

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

Object tracking is a critical task in automatic security precaution systems. In order to precisely track objects, two major problems have to be solved. First, when the background and the object are similar, the tracking center of the object will be drifted to the background. Second, when the scales of an object are changed, the object will be lost in tracking. The widely used object tracking algorithms, e.g. Mean-Shift, Particle Filter etc., cannot effectively solve these problems. In this paper, we proposed SSATMI, a scale self-adaptive tracking method based on moment invariants. In SSATMI, to solve the first problem, a joint feature histogram, called spatial histogram, is proposed to represent the object. The spatial histogram includes not only color information but also spatial information so that it can describe the object more precisely and robustly for tracking. In addition, a novel kernel function is proposed for the joint feature histogram to improve the computation efficiency. To solve the second problem, HU moment invariant is applied to modify the kernel bandwidth of the candidate object. The SSATMI has been evaluated in a PC on a video from VIRAT Ground Video Dataset, a video taken under scale changing and light changing, and a video taken under scale changing and occluding. The experimental results show that SSATMI is adaptive to scale changing and light changing, and robust to partial occlusion. In addition, the tracking speed is fast enough for real-time tracking applications.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7

Similar content being viewed by others

References

  1. Okuma, K., Taleghani, A., De Freitas, N., Little, J., Lowe, D. (2004). A boosted particle filter: Multitarget detection and tracking. Computer Vision - ECCV, 3021, 28–39.

    Google Scholar 

  2. Comaniciu, D., Ramesh, V., Meer, P. (2003). Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5), 564–577.

    Article  Google Scholar 

  3. Fukunaga, K., & Hostetler, L. (1975). The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 21(1), 32–40.

    Article  MathSciNet  Google Scholar 

  4. Ning, J., Zhang, L., Zhang, D., Wu, C. (2012). Scale and orientation adaptive mean shift tracking. Computer Vision, IET, 6(1), 52–61.

    Article  MathSciNet  Google Scholar 

  5. Yilmaz, A. (2011). Kernel-based object tracking using asymmetric kernels with adaptive scale and orientation selection. Machine Vision and Applications, 22(2), 255–268.

    Article  Google Scholar 

  6. Zivkovic, Z., & Krose, B. (2004). An em-like algorithm for color-histogram-based object tracking. Computer Vision and Pattern Recognition, 1, 798–803.

    Google Scholar 

  7. Hu, J., Juan, C., Wang, J. (2008). A spatial-color mean-shift object tracking algorithm with scale and orientation estimation. Pattern Recognition Letters, 29(16), 2165–2173.

    Article  Google Scholar 

  8. Wai, R., & Lin, Y. (2013). Adaptive moving-target tracking control of vision-based mobile robot via dynamic petri recurrent-fuzzyneural-network. IEEE Transactions on Fuzzy Systems, 21, 688–701.

  9. Zhao, P., Zhu, H., Li, H., Shibata, T. (2012). A directional-edge-based real-time object tracking system employing multiple candidate-location generation. IEEE Transaction on Circuits and Systems for Video Technology, 23(3), 503–517.

    Article  Google Scholar 

  10. Zhang, P., Wang, H., Wang, W. (2011). Added markov chain monte carlo particle filtering optimized by mean shift and application in target tracking. In IEEE 13th International Conference on Communication Techonology (ICCT) (pp. 771–775).

  11. Maalouf, A., & Larabi, M. (2012). Robust foveal wavelet-based object tracking. In IEEE Internaltional Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1489–1492).

  12. Zhang, X., Hu, W., Bao, H., Maybank, S. (2013). Robust head tracking based on multiple cues fusion in the kernel-bayesian framework. IEEE Transactions on Circuits and Systems for Video Technology, 23, 1197–1208.

  13. Elgammal, R., Duraiswami, A., Davis, L. (2003). Probabilistic tracking in joint feature-spatial spaces. Computer Vision and Pattern Recognition, 1, 781–788.

    Google Scholar 

  14. Dai, Y., Wei, W., Lin, Y., Zhang, G. (2011). Enhanced mean shift tracking algorithm based on evolutive asymmetric kernel. In International Conference on Multimedia Technology (ICMT) (pp. 5394–5398).

  15. Cheng, Y. (2002). Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(8), 790–799.

    Article  Google Scholar 

  16. Lindeberg, T. (1998). Feature detection with automatic scale selection. International Journal of Computer Vision, 30(2), 79–116.

    Article  Google Scholar 

  17. Bretzner, L., & Lindeberg, T. (1999). Qualitative multi-scale feature hierarchies for object tracking. Scale-Space Theories in Computer Vision, 1682, 117–128.

    Article  Google Scholar 

  18. Birchfield, S., & Rangarajan, S. (2005). Spatiograms versus histograms for region-based tracking. In IEEE computer society conference on computer vision and pattern recognition (Vol. 2, pp. 1158-1163).

  19. Parameswaran, V., Remesh, V., Zoghlami, I. (2006). Tunable kernels for tracking. In IEEE computer society conference on computer vision and pattern recognition (Vol. 2, pp. 2179– 2186).

  20. Porikli, F., & Tuzel, O. (2005). Object tracking in low-frame-rate video. Image and Video Communications and Processing, 5685, 72–79.

    Google Scholar 

  21. Bradski, G. (1998). Computer vision face tracking for use in a perceptual user interface. IEEE Workshop on Application of Computer Vision, 149(3), 214–219.

    Google Scholar 

  22. Liu, C., & Yung, N. (2010). Scale-adaptive spatial appearance feature density approximation for object tracking. IEEE Transactions on Intelligent Transportation Systems, 12(1), 284–290.

    Article  Google Scholar 

  23. Foy, W. (2012). Position-location solutions by taylor-series estimation. IEEE Transactions on Aerospace and Electronic Systems, AES-12(2), 187–192.

    Article  Google Scholar 

  24. Abu-Mostafa, Y., & Psaltis, D. (1984). Recognitive aspects of moment invariants. IEEE Transaction on Pattern Analysis and Machine Intelligence, PAMI-6(6), 698–706.

    Article  Google Scholar 

  25. Reddi, S. (2009). Radial and angular moment invariants for image identification. IEEE Transaction on Pattern Analysis and Machine Intelligence, PAMI-3(2), 240–242.

    Article  Google Scholar 

  26. Dai, X., Zhang, H., Shu, H., Luo, L. (2010). Image recognition by combined invariants of legendre moment. In IEEE International Conference on Information and Automation (ICIA) (pp. 1793–1798).

  27. Hu, M. (1962). Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8(2), 179–187.

    Article  Google Scholar 

  28. Terrell, G., & Scott, D. (1992). Variable kernel density estimation. The Annals of Statistics, 20(3), 1236–1265.

    Article  MathSciNet  Google Scholar 

  29. Zhuge, Q., Guo, Y., Hu, J., Tseng, W.-C. (2012). Minimizing access cost for multiple types of memory units in embedded systems through data allocation and scheduling. IEEE Transactions on Signal Processing, 60(6), 3253–3263.

    Article  MathSciNet  Google Scholar 

  30. http://www.opencv.org.cn (2013).

  31. http://www.viratdata.org/ (2013).

  32. Hüllermeier, E., & Fürnkranz, J. (2010). On loss functions in label ranking and risk minimization by pairwise learning. Journal of Computer and System Sciences, 76(1), 49–62.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Software, Beihang University, Xueyuan Road 37, Haidian District, Beijing, China

    Yimei Kang & Bin Hu

  2. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Yi Wang & Zili Shao

Authors
  1. Yimei Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zili Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yimei Kang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kang, Y., Hu, B., Wang, Y. et al. A Scale Self-Adaptive Tracking Method Based on Moment Invariants. J Sign Process Syst 81, 197–212 (2015). https://doi.org/10.1007/s11265-014-0935-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-014-0935-7

Keywords

Search

Navigation