A Novel Ant Colony Detection Using Multi-Region Histogram for Object Tracking

  • Seid Miad Zandavi
  • Feng Sha
  • Vera Chung
  • Zhicheng Lu
  • Weiming Zhi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10636)

Abstract

Efficient object tracking become more popular in video processing domain. In recent years, many researchers have developed excellent models and methods for complicated tracking problems in real environment. Among those approaches, object feature definition is one of the most important component to obtain better accuracy in tracking. In this paper, we propose a novel multi-region feature selection method which defines histogram values of basic areas and random areas (MRH) and combined with continuous ant colony filter detection to represent the original target. The proposed approach also achieves smooth tracking on different video sequences, especially with Motion Blur problem. This approach is designed and tested in MATLAB 2016b environment. The experiment result demonstrates better and faster tracking performance and shows continuous tracking trajectory and competitive outcomes regarding to traditional methods.

Keywords

Multi-Region Histogram Ant colony filter Histogram 

References

  1. 1.
    Zhang, L., van der Maaten, L.: Structure preserving object tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1838–1845 (2013)Google Scholar
  2. 2.
    Nobahari, H., Sharifi, A.: Continuous ant colony filter applied to online estimation and compensation of ground effect in automatic landing of quadrotor. Eng. Appl. Artif. Intell. 32, 100–111 (2014)CrossRefGoogle Scholar
  3. 3.
    Nobahari, H., Sharifi, A.: A novel heuristic filter based on ant colony optimization for non-linear systems state estimation. In: Li, Z., Li, X., Liu, Y., Cai, Z. (eds.) ISICA 2012. CCIS, pp. 20–29. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-34289-9_3 CrossRefGoogle Scholar
  4. 4.
    Nobahari, H., Zandavi, S.M., Mohammadkarimi, H.: Simplex filter: a novel heuristic filter for nonlinear systems state estimation. Appl. Soft Comput. 49, 474–484 (2016)CrossRefGoogle Scholar
  5. 5.
    Franken, N.: Visual exploration of algorithm parameter space. In: IEEE Congress on Evolutionary Computation, CEC 2009, pp. 389–398. IEEE (2009)Google Scholar
  6. 6.
    Wu, Y., Lim, J., Yang, M.-H.: Online object tracking: a benchmark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411–2418 (2013)Google Scholar
  7. 7.
    OpenCV 2.4.9.0 documentation. http://docs.opencv.org/2.4.9/
  8. 8.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, pp. 1150–1157. IEEE (1999)Google Scholar
  9. 9.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110, 346–359 (2008)CrossRefGoogle Scholar
  10. 10.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: 2011 IEEE international conference on Computer Vision (ICCV), pp. 2564–2571. IEEE (2011) Google Scholar
  11. 11.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, pp. 886–893. IEEE (2005)Google Scholar
  12. 12.
    Sha, F., Bae, C., Liu, G., Zhao, X., Chung, Y.Y., Yeh, W.: A categorized particle swarm optimization for object tracking. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2737–2744. IEEE (2015)Google Scholar
  13. 13.
    Liu, G., Chen, Z., Yeung, H.W.F., Chung, Y.Y., Yeh, W.-C.: A new weight adjusted particle swarm optimization for real-time multiple object tracking. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9948, pp. 643–651. Springer, Cham (2016). doi: 10.1007/978-3-319-46672-9_72 CrossRefGoogle Scholar
  14. 14.
    Choi, J., Jin Chang, H., Jeong, J., Demiris, Y., Young Choi, J.: Visual tracking using attention-modulated disintegration and integration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4321–4330 (2016)Google Scholar
  15. 15.
    Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis, Politecnico di Milano, Italy (1992)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Seid Miad Zandavi
    • 1
  • Feng Sha
    • 1
  • Vera Chung
    • 1
  • Zhicheng Lu
    • 1
  • Weiming Zhi
    • 2
  1. 1.School of Information TechnologiesUniversity of SydneySydneyAustralia
  2. 2.Department of Engineering ScienceUniversity of AucklandAucklandNew Zealand

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