Skip to main content
SpringerLink
Log in

Scale and Orientation-Based Background Weighted Histogram for Human Tracking

  • 3DR Express
  • Published:
3D Research

Abstract

The Mean Shift procedure is a popular object tracking algorithm since it is fast, easy to implement and performs well in a range of conditions. However, classic Mean Shift tracking algorithm fixes the size and orientation of the tracking window, which limits the performance when the target’s orientation and scale change. In this paper, we present a new human tracking algorithm based on Mean Shift technique in order to estimate the position, scale and orientation changes of the target. This work combines moment features of the weight image with background information to design a robust tracking algorithm entitled Scale and Orientation-based Background Weighted Histogram (SOBWH). The experimental results show that the proposed approach SOBWH presents a good compromise between tracking precision and calculation time, also they validate its robustness, especially to large background variation, scale and orientation changes and similar background scenes.

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

Similar content being viewed by others

Notes

  1. The profile of a kernel K is defined as a function \(k{:}\, \left[ {\begin{array}{*{20}c} 0 & \infty \\ \end{array} } \right[ \to R\) such that \(K\left( z \right) = k\left( {\left\| z \right\|^{2} } \right)\) [4].

References

  1. Bousetouane, F., Dib, L., & Snoussi, H. (2013). Improved mean shift integrating texture and color features for robust real time object tracking. The Visual Computer, 29(3), 155–170.

    Article  Google Scholar 

  2. Bradski, G. (1998). Computer vision face tracking for use in a perceptual user interface. Intel Technology Journal, 2(2), 1–15.

    Google Scholar 

  3. Collins, R. T. (2003). Mean-shift blob tracking through scale space. In Proceedings of IEEE conference on computer vision and pattern recognition, vol. 2 (pp. 234–240).

  4. 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 

  5. Fukunaga, K., & Hostetler, L. D. (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  MATH  Google Scholar 

  6. Georgescu, B., & Meer, P. (2004). Point matching under large image deformations and illumination changes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 674–688.

    Article  Google Scholar 

  7. Horn, R. A., & Johnson, C. R. (1991). Topics in matrix analysis. Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  8. http://www.visual-tracking.net/.

  9. Hu, J. S., Juan, C. W., & Wang, J. 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 

  10. Kailath, T. (1967). The divergence and Bhattacharyya distance measures in signal selection. IEEE Transactions on Communication Technology, 15, 52–60.

    Article  Google Scholar 

  11. Laaroussi, K., Saaidi, A., Masrar, M., Satori, K. (2016). Human tracking based on appearance model. In Proceedings of the mediterranean conference on information & communication technologies, vol. 1 (pp. 297–305). Springer International Publishing.

  12. Laaroussi, K., Saaidi, A., Masrar, M., & Satori, K. (2014). Video-surveillance system for tracking moving people using color interest points. World Applied Sciences Journal, 32(2), 289–301.

    Google Scholar 

  13. Laaroussi, K., Saaidi, A., & Satori, K. (2014). People tracking using color control points and skin color. Journal of emerging technologies in web intelligence, 6(1), 94–100.

    Article  Google Scholar 

  14. Liang, D., Huang, Q., Jiang, S., Yao, H., Gao, W. (2007). Mean shift blob tracking with adaptive feature selection and scale adaptation. In Proceedings international conference image processing, vol. 3 (pp. III-369).

  15. Liang, Z. P., & Lauterbur, P. C. (2000). Principles of magnetic resonance imaging. Bellingham: SPIE Optical Engineering Press.

    Google Scholar 

  16. Li, S., Chang, H., & Zhu, C. (2010). Adaptive pyramid mean shift for global real time visual tracking. Image and Vision Computing, 28(3), 424–437.

    Article  Google Scholar 

  17. Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., & Hengel, A. V. D. (2013). A survey of appearance models in visual object tracking. ACM Transactions on Intelligent Systems and Technology, 4(4), 58.

    Article  Google Scholar 

  18. Mukundan, R., & Ramakrishnan, K. R. (1998). Moment functions in image analysis: theory and applications, vol. 100. Singapore: World Scientific.

    Book  MATH  Google Scholar 

  19. Ning, J., Zhang, L., Zhang, D., & Wu, C. (2010). Robust mean shift tracking with corrected background-weighted histogram. IET Computer Vision, 6(1), 62–69.

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  21. Parameswaran, V., Ramesh, V., Zoghlami, I. (2006). Tunable kernels for tracking. In Proceedings IEEE computer society conference on computer vision and pattern recognition (pp. 2179–2186).

  22. Peng, N. S., Yang, J., Liu, Z., & Zhang, F. C. (2005). Automatic selection of kernel-bandwidth for mean shift object tracking. Journal of Software, 16(9), 1542–1550.

    Article  Google Scholar 

  23. Prokop, R. J., & Reeves, A. P. (1992). A survey of moment based techniques for unoccluded object representation and recognition. Graphical Models and Image Processing, 54(5), 438–460.

    Article  Google Scholar 

  24. Qingchang, G., Xiaojuan, C., Hongxia, C. (2007). Mean shift of variable window based on the Epanechnikov Kernel. In Proceeding international conference on mechatronics and automation (pp. 2314–2319).

  25. Senior, A., Hampapur, A., Tian, Y., Brown, L., Pankanti, S., & Bolle, R. (2006). Appearance models for occlusion handling. Image and Vision Computing, 24(11), 1233–1243.

    Article  Google Scholar 

  26. Vojir, T., Noskova, J., & Matas, J. (2014). Robust scale-adaptive mean shift for tracking. Pattern Recognition Letters, 49, 250–258.

    Article  Google Scholar 

  27. Yang, C., Duraiswami, R., Davis, L. (2005). Efficient Mean Shift tracking via a new similarity measure. In Proceeding computer vision and pattern recognition, vol. 1 (pp. 176–183).

  28. Yilmaz, A., Javed, O., & Shah, M. (2006). Object tracking: a survey. ACM Computer Survey, 38(4), 13.

    Article  Google Scholar 

  29. Yu, W., Tian, X., Hou, Z., Zha, Y., & Yang, Y. (2015). Multi-scale mean shift tracking. Computer Vision, IET, 9(1), 110–123.

    Article  Google Scholar 

  30. Zhao, C., Knight, A., Reid, I. (2008). Target tracking using mean shift and affine structure. In Proceeding of the 19th international conference in pattern recognition (pp. 1–5).

  31. Zhou, H., Yuan, Y., & Shi, C. (2009). Object tracking using SIFT features and mean shift. Computer Vision and Image Understanding, 113(3), 345–352.

    Article  Google Scholar 

  32. Zivkovic, Z., Kröse, B. (2004). An EM-like algorithm for color histogram based object tracking. In Proceeding IEEE conference computer vision and pattern recognition, vol. 1 (pp. 798–803).

Download references

Author information

Authors and Affiliations

  1. LIIAN, Department of Mathematics and Computer Science, Faculty of Science Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, P.O 1796, Atlas, Fez, Morocco

    Khadija Laaroussi, Abderrahim Saaidi, Mohamed Masrar & Khalid Satori

  2. LSI, Laboratory of Engineering Sciences, Polydisciplinary Faculty of Taza, Sidi Mohamed Ben Abdellah University, P.O 1223, Taza, Morocco

    Abderrahim Saaidi

Authors
  1. Khadija Laaroussi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abderrahim Saaidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed Masrar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Khalid Satori
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khadija Laaroussi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Laaroussi, K., Saaidi, A., Masrar, M. et al. Scale and Orientation-Based Background Weighted Histogram for Human Tracking. 3D Res 7, 21 (2016). https://doi.org/10.1007/s13319-016-0097-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13319-016-0097-4

Keywords

Search

Navigation