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Visual Tracking Using Harmony Search

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 270))

Abstract

In this chapter we present a novel method for tracking an arbitrary target through a video sequence using the Harmony Search algorithm called the Harmony Filter. The Harmony Filter models the target using a color histogram and compares potential matches in each video frame using the Bhattacharyya coefficient. Matches are found using the Improved Harmony Search (IHS) algorithm. Experimental results show that the Harmony Filter can robustly track targets in challenging environments while still maintaining real-time performance. We compare the runtime and accuracy performance of the Harmony Filter with other popular methods used in visual tracking including the particle filter and the Kalman Filter. We show that the Harmony filter performs better in both speed and accuracy than similar systems based on the particle filter and the Unscented Kalman Filter (UKF).

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References

  1. Gutman, P., Velger, M.: Tracking Targets Using Adaptive Kalman Filtering. IEEE Trans. On Aerospace and Electronic Systems 26, 691–699 (1990)

    Article  Google Scholar 

  2. Li, M., Hong, B., Cai, Z., Luo, R.: Novel Rao-Blackwellized Particle Filter for Mobile Robot SLAM Using Monocular Vision. International Journal of Intelligent Technology 1(1), 63–69 (2006)

    Google Scholar 

  3. Li, P., Zhang, T., Ma, B.: Unscented Kalman Filter for Visual Curve Tracking. Image and Vision Computing, 157–164 (2004)

    Google Scholar 

  4. Isard, M., Blake, A.: Condensation – Conditional Density Propagation for Visual Tracking. IJCV 29(1), 5–28 (1998)

    Article  Google Scholar 

  5. Minami, M., Agbanhan, J., Asakura, T.: Manipulator Visual Servoing and Tracking of Fish Using a Genetic Algorithm. Industrial Robot: An International Journal 26(4), 278–289 (1999)

    Article  Google Scholar 

  6. Morsley, Y., Djouadi, M.S.: Genetic Algorithm Combined to IMM Approach for Tracking Highly Maneuvering Targets. IAENG International Journal of Computer Science 35 (2008); advanced online publication 19 February 2008

    Google Scholar 

  7. Sulistijono, I.A., Kubota, N.: Human Head Tracking Based on Particle Swarm Optimisation and Genetic Algorithm. Journal of Advanced Computational Intelligence and Intelligent Informatics 11(6), 681–687 (2007)

    Google Scholar 

  8. Fourie, J., Mills, S., Green, R.: Visual Tracking Using the Harmony Search Algorithm. In: IVCNZ 23rd International Conference on Image and Vision Computing New Zealand, pp. 1–6 (2008)

    Google Scholar 

  9. Kailath, T.: The Divergence and Bhattacharyya Distance Measures in Signal Selection. IEEE Trans. On Comm. Technology 15(1), 52–60 (1967)

    Article  Google Scholar 

  10. Comaniciu, D., Ramesh, V., Meer, P.: Kernel-Based Object Tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–575 (2003)

    Article  Google Scholar 

  11. Mahdavi, M., Fesanghary, M., Damangir, E.: An Improved Harmony Search Algorithm for Solving Optimization Problems. Applied Mathematics and Computation 188, 1567–1579 (2007)

    Article  MATH  MathSciNet  Google Scholar 

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Fourie, J., Mills, S., Green, R. (2010). Visual Tracking Using Harmony Search. In: Geem, Z.W. (eds) Recent Advances In Harmony Search Algorithm. Studies in Computational Intelligence, vol 270. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04317-8_4

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  • DOI: https://doi.org/10.1007/978-3-642-04317-8_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04316-1

  • Online ISBN: 978-3-642-04317-8

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