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ACO–MKFCM: An Optimized Object Detection and Tracking Using DNN and Gravitational Search Algorithm

  • T. MahalingamEmail author
  • M. Subramoniam
Article
  • 18 Downloads

Abstract

Object tracking is a dynamic optimization process based on the temporal information related to the previous frames. Proposing a method with higher precision in complex environments is a challenge for researchers in the field of study. In this research, efficient object detection and movement tracking video is proposed. Here we are considering the input video sequence is PETS and Hall monitor video. Kernel fuzzy c-means procedure still endures several downsides, such as reduced convergence rate, obtaining stuck in the local minima as well as at risk to initialization level of sensitivity. Ant colony optimization algorithm is a new population-based optimization technique that has actually been utilized effectively for resolving numerous complicated difficulties. This research suggested a new strategy called ACO–MKFCM to resolve MKFCM initialization issue making use of ant colony optimization (ACO) algorithm to locate optimum first cluster centres for the MKFCM, hence enhance all applications associated fuzzy clustering such as foreground segmentation in image processing. Initially, the background and foreground separation is done by hybridization of modified kernel fuzzy c means algorithm (MKFCM) with ant colony optimization. The recommended new technique is intellectual and also vibrant clustering method for splitting up of non-static object. Then object detection and tracking is done by gravitational search algorithm based deep belief neural network. The implementation will be in MATLAB. The performance of the suggested technique is appraised by means of precision, recall, F-measure, FPR, FNR, PWC, FAR, similarity, specificity, and accuracy. From the empirical effects, the future work outperforms than the state of art work. Here the proposed method attains maximum precision and recall value for both PETS and Hall monitor video when analyzed to the existent algorithm.

Keywords

Modified kernel fuzzy c-means Ant colony optimization (ACO) Deep belief neural network Gravitational search algorithm Foreground Background Clustering 

Notes

References

  1. 1.
    Sabirin, H., & Kim, M. (2012). Moving object detection and tracking using a spatio-temporal graph in H. 264/AVC bit streams for video surveillance. IEEE Transactions on Multimedia, 14(3), 657–668.CrossRefGoogle Scholar
  2. 2.
    Zhang, S., Wang, C., Chan, S.-C., Wei, X., & Ho, C.-H. (2015). New object detection, tracking, and recognition approaches for video surveillance over camera network. IEEE Sensors Journal, 15(5), 2679–2691.CrossRefGoogle Scholar
  3. 3.
    Cai, L., He, L., Xu, Y., Zhao, Y., & Yang, X. (2010). Multi-object detection and tracking by stereo vision. Pattern Recognition, 43(12), 4028–4041.CrossRefGoogle Scholar
  4. 4.
    Chen, Y., Shen, Y., Liu, X., & Zhong, B. (2015). 3D object tracking via image sets and depth-based occlusion detection. Signal Processing, 112, 146–153.CrossRefGoogle Scholar
  5. 5.
    Subudhi, B. N., Nanda, P. K., & Ghosh, A. (2011). A change information based fast algorithm for video object detection and tracking. IEEE Transactions on Circuits and Systems for Video Technology, 21(7), 993–1004.CrossRefGoogle Scholar
  6. 6.
    del-Blanco, C. R., Jaureguizar, F., & Garcia, N. (2012). An efficient multiple object detection and tracking framework for automatic counting and video surveillance applications. IEEE Transactions on Consumer Electronics, 58(3), 857–862.CrossRefGoogle Scholar
  7. 7.
    Kanagamalliga, S., & Vasuki, S. (2018). Contour-based object tracking in video scenes through optical flow and gabor features. Optik-International Journal for Light and Electron Optics, 157, 787–797.CrossRefGoogle Scholar
  8. 8.
    Riahi, D., & Bilodeau, G.-A. (2016). Online multi-object tracking by detection based on generative appearance models. Computer Vision and Image Understanding, 152, 88–102.CrossRefGoogle Scholar
  9. 9.
    Tian, S., Yuan, F., & Xia, G.-S. (2016). Multi-object tracking with inter-feedback between detection and tracking. Neuro Computing, 171, 768–780.Google Scholar
  10. 10.
    Mahalingam, T., & Subramoniam, M. (2019). A competent frame work for efficient object detection, tracking and classification. Wireless Personal Communications, 107(2), 939–957.CrossRefGoogle Scholar
  11. 11.
    Mirunalini, P., Jaisakthi, S. M., & Sujana, R. (2017). Tracking of object in occluded and non-occluded environment using SIFT and Kalman Filter. In 2017 IEEE region 10 conference (TENCON) (pp. 1290–1295).Google Scholar
  12. 12.
    Hu, W.-C., Chen, C.-H., Chen, T.-Y., Huang, D.-Y., & Wu, Z.-C. (2015). Moving object detection and tracking from video captured by moving camera. Journal of Visual Communication and Image Representation, 30, 164–180.CrossRefGoogle Scholar
  13. 13.
    Prasad, D. K., Rajan, D., Rachmawati, L., Rajabally, E., & Quek, C. (2017). Video processing from electro-optical sensors for object detection and tracking in a maritime environment: A survey. IEEE Transactions on Intelligent Transportation Systems, 18(8), 1993–2016.CrossRefGoogle Scholar
  14. 14.
    Azab, M. M., Shedeed, H. A., & Hussein, A. S. (2014). New technique for online object tracking-by-detection in video. IET Image Processing, 8(12), 794–803.CrossRefGoogle Scholar
  15. 15.
    Mehdizadeh, E., & Golabzaei, A. (2016). Electrical fuzzy C-means: A new heuristic fuzzy clustering algorithm. Journal Cogent Engineering, 3(1), 1208397.Google Scholar
  16. 16.
    Benabdellah, N. C., Gharbi, M., & Bellafkih, M. (2013). Learner’s profile definition: Fuzzy logic application. International Journal of Computer Science and Electronics Engineering, 1(4), 542–546.Google Scholar
  17. 17.
    Benabdellah, N. C., Gharbi, M., & Bellafkih, M. (2013). Content adaptation and learner profi le defi nition: Ant colony algorithm application. Phil. Sita13, IEEExplorer.Google Scholar
  18. 18.
    Ruan, C., Jaggi, J., Xue, J., & Fadili, D. Bloyet. (2010). Brain tissue classifi cation of magnetic resonance images using partial volume modeling. IEEE Transactions on Medical Imaging, 19(12), 1179–1187.CrossRefGoogle Scholar
  19. 19.
    Heimann, T., & Meinzer, H. P. (2009). Statistical shape models for 3D medical image segmentation: A review. Medical Image Analysis, 13(4), 543–563.CrossRefGoogle Scholar
  20. 20.
    Klauschen, F., Goldman, A., Barra, V., Meyer-Lindenberg, A., & Lundervold, A. (2009). Evaluation of auto-mated brain MR image segmentation and volumetry methods. Human Brain Mapping, 30(4), 1310–1327.CrossRefGoogle Scholar
  21. 21.
    Pintea, C. M., & Ticala, C. (2016). Medical image processing: A brief survey and a new theoretical hybrid ACO model. Combinations of Intelligent Methods and Applications (pp. 117–134). Cham: Springer.CrossRefGoogle Scholar
  22. 22.
    Agarwal, P., Singh, R., & Agarw, P. (2015). A Combination of bias-field corrected fuzzy c-means and level set approach for brain mri image segmentation. In IEEE international conference on soft computing and machine intelligence.Google Scholar
  23. 23.
    Xu, X., Liang, T., Wang, G., & Wang, M. (2016). Self-adaptive PCNN based on the ACO algorithm and its application on medical image segmentation. Journal of Intelligent Automation & Soft Computing, 23(2), 303–310.CrossRefGoogle Scholar
  24. 24.
    Bonabeau, E., Dorigo, M., & Theraulez, G. (1999). Swarm intelligence: From natural to artificial systems. New York: Oxford University Press.zbMATHGoogle Scholar
  25. 25.
    Maniezzo, V., & Conlorni, A. (1999). The ant system applied to the quadratic assignment problem. IEEE Transactions on Knowledge and Data Engineering, 11(5), 769–778.CrossRefGoogle Scholar
  26. 26.
    Dorigo, M., Maniezzo, V., & Colorni, A. (1996). The ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B, 26(1), 29–41.CrossRefGoogle Scholar
  27. 27.
    Shuai, H., Liu, Q., Zhang, K., Yang, J., & Deng, J. (2017). Cascaded regional spatio-temporal feature-routing networks for video object detection. IEEE Access, 6, 3096–3106.CrossRefGoogle Scholar
  28. 28.
    Zhang, S., Yu, X., Sui, Y., Zhao, S., & Zhang, L. (2015). Object tracking with multi-view support vector machines. IEEE Transactions on Multimedia, 17(3), 265–278.Google Scholar
  29. 29.
    Laumer, M., Amon, P., Hutter, A., & Kaup, A. (2016). Moving object detection in the H. 264/AVC compressed domain. APSIPA Transactions on Signal and Information Processing, 5, e18.CrossRefGoogle Scholar
  30. 30.
    Pawaskar, M. C., Narkhede, N. S., & Athalye, S. S. (2014). Detection of moving object based on background subtraction. International Journal of Emerging Trends & Technology in Computer Science, 7(3), 215–218.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Sathyabama UniversityChennaiIndia

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