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Journal of Real-Time Image Processing

, Volume 15, Issue 4, pp 799–816 | Cite as

Object tracking by mean shift and radial basis function neural networks

  • Vahid Rowghanian
  • Karim Ansari-Asl
Original Research Paper
  • 154 Downloads

Abstract

In this paper, a tracker based on mean shift and radial basis function neural networks called MS-RBF is addressed. As its name implies, two independent trackers have been combined and linked together. The mean shift algorithm estimates the target’s location within only two iterations. The scale and orientation of target are computed by exploiting 2-D correlation coefficient between reference and target candidate histograms instead of using Bhattacharyya coefficient. A code optimization strategy, named multiply–add–accumulate (MAC), is proposed to remove useless memory occupation and programmatic operations. MAC implementation has reduced computational load and made overall tracking process faster. The second tracker “RBFNN” has an input feature vector that contains variables such as local contrast, color histogram, gradient, intensity, and spatial frequency. The neural network learns the color and texture features from the target and background. Then, this information is used to detect and track the object in other frames. The neural network employs Epanechnikov activation functions. The features extracted in any frame are clustered by Fuzzy C-Means clustering which produces the means and variances of the clusters. The experimental results show that the proposed tracker can resist to different types of occlusions, sudden movement, and shape deformations.

Keywords

Real-time object tracking Radial basis functions Neural network Anisotropic mean shift object tracking Algorithm optimization Up-sampling 

References

  1. 1.
    Arora, A., Dutta, P., Bapat, S., Kulathumani, V., Zhang, H., Naik, V., et al.: A line in the sand: a wireless sensor network for target detection, classification, and tracking. Comput. Netw. 46, 605–634 (2004)CrossRefGoogle Scholar
  2. 2.
    Velastin, S., Yin, J., Davies, A., Vicencio-Silva, M., Allsop, R., Penn, A.:Automated measurement of crowd density and motion using image processing. In: Proceeding of Seventh International Conference On Road Traffic Monitoring And Control, 26–28 April 1994 (IEE Conference Publication 391) (1994)Google Scholar
  3. 3.
    Azuma, R.T.: A survey of augmented reality. Presence 6, 355–385 (1997)CrossRefGoogle Scholar
  4. 4.
    Ubillos, R.: Method and apparatus for video editing with video clip representations displayed along a time line. ed: Google Patents (1999)Google Scholar
  5. 5.
    Mountney, P., Stoyanov, D., Yang, G.-Z.: Three-dimensional tissue deformation recovery and tracking. Sig. Process. Mag. IEEE 27, 14–24 (2010)CrossRefGoogle Scholar
  6. 6.
    Khan, Z.H., Gu, I., Backhouse, A.G.: Robust visual object tracking using multi-mode anisotropic mean shift and particle filters. IEEE Trans.Circ. Syst. Video Technol. 21, 74–87 (2011)CrossRefGoogle Scholar
  7. 7.
    Babu, R.V., Parate, P.: Robust tracking with interest points: a sparse representation approach. Image Vis. Comput. 33, 44–56 (2015)CrossRefGoogle Scholar
  8. 8.
    Zhang, K., Zhang, L., Yang, M.-H.,: Real-time compressive tracking, in Computer Vision–ECCV, 2012, ed: Springer, pp. 864–877 (2012)Google Scholar
  9. 9.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision, in IJCAI, pp. 674–679 (1981)Google Scholar
  10. 10.
    Black, M.J., Anandan, P.: The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Comput. Vis. Image Underst. 63, 75–104 (1996)CrossRefGoogle Scholar
  11. 11.
    Horn, B.K., Schunck, B.G.: Determining optical flow. In: Technical Symposium East, pp. 319–331 (1981)Google Scholar
  12. 12.
    Yilmaz, A., Li, X., Shah, M.: Contour-based object tracking with occlusion handling in video acquired using mobile cameras. IEEE Trans. Pattern Anal. Mach. Intel. 26, 1531–1536 (2004)CrossRefGoogle Scholar
  13. 13.
    Zhao, P., Zhu, H., Li, H., Shibata, T.: A directional-edge-based real-time object tracking system employing multiple candidate-location generation. IEEE Trans. Circ. Syst. Video Technol. 23, 503–517 (2013)CrossRefGoogle Scholar
  14. 14.
    Botella, G., Martín, H.J.A., Santos, M., Meyer-Baese, U.: FPGA-based multimodal embedded sensor system integrating low-and mid-level vision. Sensors 11, 8164–8179 (2011)CrossRefGoogle Scholar
  15. 15.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intel. 24, 603–619 (2002)CrossRefGoogle Scholar
  16. 16.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Math. Intel. 25, 564–577 (2003)CrossRefGoogle Scholar
  17. 17.
    Kailath, T.: The divergence and Bhattacharyya distance measures in signal selection. IEEE Trans. Pattern Anal. Mach. Intel. 15, 52–60 (1967)Google Scholar
  18. 18.
    Bradski, G. R.: Computer vision face tracking for use in a perceptual user interface (1998)Google Scholar
  19. 19.
    Zivkovic, Z., Krose, B.: An EM-like algorithm for color-histogram-based object tracking. In: Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, vol. 1, pp. I-798-I-803 (2004).Google Scholar
  20. 20.
    Carreira-Perpindn, M.: Gaussian mean-shift is an EM algorithm. IEEE Trans. Pattern Anal. Mach. Intel. 29, 767–776 (2007)CrossRefGoogle Scholar
  21. 21.
    Shan, C., Wei, Y., Tan, T., Ojardias, F.: Real time hand tracking by combining particle filtering and mean shift. In: Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on, pp. 669–674 (2004)Google Scholar
  22. 22.
    Shan, C., Tan, T., Wei, Y.: Real-time hand tracking using a mean shift embedded particle filter. Pattern Recog. 40, 1958–1970 (2007)CrossRefGoogle Scholar
  23. 23.
    Chen, Z.: Bayesian filtering: from Kalman filters to particle filters, and beyond. Statistics 182, 1–69 (2003)CrossRefGoogle Scholar
  24. 24.
    Rowghanian, V., Asl, K.A.: Non iterated mean shift and particle filtering. In: Iranian Conference on Electrical Engineering (ICEE), 22nd, pp. 226–231 (2014)Google Scholar
  25. 25.
    Chen, Z., Husz, Z.L., Wallace, I., Wallace, A.M.: Video object tracking based on a Chamfer distance transform. In: Image Processing, 2007. ICIP 2007. IEEE International Conference on, 2007, pp. III-357-III-360Google Scholar
  26. 26.
    Babu, R.V., Suresh, S., Makur, A.: Online adaptive radial basis function networks for robust object tracking. Comput. Vis. Image Underst. 114, 297–310 (2010)CrossRefGoogle Scholar
  27. 27.
    Black, M.J., Jepson, A.D.: Eigentracking: robust matching and tracking of articulated objects using a view-based representation. Int. J. Comput. Vis. 26, 63–84 (1998)CrossRefGoogle Scholar
  28. 28.
    Huang, G.-B., Siew, C.-K.: Extreme learning machine: RBF network case. In: Control, Automation, Robotics and Vision Conference. ICARCV 8th, pp. 1029–1036 (2004)Google Scholar
  29. 29.
    Bin, Z., Junzheng, W., Jiali, M.: Algorithm of target tracking based on mean shift with RBF neural network. In: Chinese Control Conference ,CCC 27th, pp. 518–521 (2008)Google Scholar
  30. 30.
    Meyer-Bäse, A., Botella, G., Rybarska-Rusinek, L.: Stochastic stability analysis of competitive neural networks with different time-scales. Neurocomputing 118, 115–118 (2013)CrossRefGoogle Scholar
  31. 31.
    Shi, C., Brodersen, R.W.: Floating-point to fixed-point conversion with decision errors due to quantization. In: Acoustics, Speech, and Signal Processing, 2004. Proceedings.(ICASSP’04). IEEE International Conference on, 2004 vol. 5, pp. V-41-4 (2004)Google Scholar
  32. 32.
    Botella, G., Meyer-Baese, U., García, A., Rodríguez, M.: Quantization analysis and enhancement of a VLSI gradient-based motion estimation architecture. Digit. Signal Proc. 22, 1174–1187 (2012)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Bârleanu, A., Băitoiu, V., Stan, A.: Floating-point to fixed-point code conversion with variable trade-off between computational complexity and accuracy loss. In: System Theory, Control, and Computing (ICSTCC), 2011 15th International Conference on, pp. 1–6 (2011)Google Scholar
  34. 34.
    Oshiro, M., Nishimura, T.: US image improvement using fuzzy Neural Network with Epanechnikov kernel. In: Industrial Electronics, 2009. IECON’09. 35th Annual Conference of IEEE, pp. 2130–2135 (2009)Google Scholar
  35. 35.
    Jack, K.: Video demystified: a handbook for the digital engineer. Newnes, Boston (2005)Google Scholar
  36. 36.
    Ning, J., Zhang, L., Zhang, D., Wu, C.: Scale and orientation adaptive mean shift tracking. IET Comput. Vis. 6, 52–61 (2012)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Ning, J., Zhang, L., Zhang, D., Wu, C.: Robust mean-shift tracking with corrected background–weighted histogram. IET Comput. Vis. 6, 62–69 (2012)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Asvadi, A., Karami, M., Baleghi, Y.: Efficient object tracking using optimized K-means segmentation and radial basis function neural networks. Int. J. Inf. Commun. Technol. 4(1), 29–39 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Faculty of EngineeringShahid Chamran University of AhvazAhvazIran

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