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A novel visual tracking method using stochastic fractal search algorithm

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Abstract

Recently metaheuristic algorithms have attracted the attention of many researchers in various disciplines for their simplicity of application and their efficiency. Visual tracking is one of the most promising fields of application of these methods, and although many approaches have been proposed, their main disadvantage is the convergence at local minima which make them unable to find the exact position. To overcome this drawback, we propose to use an algorithm that provides an efficient exploration of the search space, which is stochastic fractal search (SFS) algorithm. SFS is used as a localization method, to find the most similar candidate to a previous defined template. Standard kernel-based spatial color histogram of the object bounding box, is evaluated in order to model the object appearance. Subsequently, Bhattacharyya distance is measured between the two histograms of the model and the candidate to define the fitness function, in which optimization is sought. To assess fairly the robustness of our approach, we have evaluated its performance on 20 video sequences from the OTB-100 sequences dataset and compared it to 11 other state-of-the-art trackers. Quantitative and qualitative evaluations on challenging situations provided satisfying results of SFS-based tracker compared to other state-of-the-art algorithms.

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References

  1. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 1–45 (2006)

    Article  Google Scholar 

  2. Mingyang,. G., et al. (2018) Real-time event-triggered object tracking in the presence of model drift and occlusion. IEEE Trans. Ind. Electron. 66(3)

  3. Pranay K., et al. (2018). Visual tracking with breeding fireflies using brightness from background-foreground information. In: 24th International Conference on Pattern Recognition (ICPR)

  4. Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. PAMI 25(5), 564–577 (2003)

    Article  Google Scholar 

  5. Zhou, H., Yuan, Y., Shi, C.: Object tracking using sift features and mean shift. Comput. Vis. Image Underst. 113(3), 345–352 (2009)

    Article  Google Scholar 

  6. Medouakh, S., Boumehraz, M., Terki, N.: Improved object tracking via joint color-LPQ texture histogram based mean shift algorithm. SIViP 12, 583–590 (2018)

    Article  Google Scholar 

  7. Gao, M., Shen, J., Yin, L., et al.: A novel visual tracking method using bat algorithm. Neurocomputing 177, 612–619 (2016)

    Article  Google Scholar 

  8. Perez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In ECCV (2002)

  9. Zhang, X.Q., Hu, W.M., Maybank, S., Li, X., Zhu, M.L.: Sequential particle swarm optimization for visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Anchorage, AK, USA, pp. 1–8 (2008)

  10. Gao, M.L., Yin, L.J., Zou, G.F., Li, H.T., Liu, W.: Visual tracking method based on cuckoo search. Opt. Eng. 54(7), 073105 (2015)

    Article  Google Scholar 

  11. Liu, G., Chung, Y.Y., Yeh, W-C: A simplified swarm optimization for object tracking. In: International Joint Conference on Neural Networks (IJCNN) 2016, pp. 169–176 (2016).

  12. Misra, R., Ray, K.S.: Object tracking based on quantum particle swarm optimization, arXiv preprint arXiv:1707.05228 (2017).

  13. A Novel Visual Tracking Method Based On Moth-Flame Optimization Algorithm: First Chinese Conference, PRCV 2018, Guangzhou, China, November 23–26, 2018 Proc. IV. doi: 10.1007/978-3-030-03341-5_24 (2018)

  14. Meng, O.K., Pauline, O., Kiong, S.C., Soong, L.E., Kiow, L.W.: A novel real time visual tracking method using modified flower pollination algorithm. In: IOP Conference Series: Journal of Physics: Conference Series 1150-012023, (2019).

  15. Salimi, H.: Stochastic fractal search: a powerful metaheuristic algorithm. Knowl-Based Syst 75, 1–18 (2015)

    Article  Google Scholar 

  16. Wu, Y., Lim, J., Yang, M.-H.: Online object tracking: a benchmark. In: Proceedings of IEEE Conference Computer Visual Pattern Recognition, pp. 2411–241 (2013)

  17. Henriques, J.A.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. (2014)

  18. Hare, S., Saffari, A., Torr, P.H.S.: Struck: structured output tracking with Kernels. In ICCV, (2011)

  19. Jia, X., Lu, H., Yang, M.-H.: Visual tracking via adaptive structural local sparse appearance model. In CVPR (2012)

  20. Zhong, W., Lu, H., Yang, M.-H.: Robust object tracking via sparsity-based collaborative model. In CVPR, (2012)

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Correspondence to Djemai Charef-Khodja.

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Charef-Khodja, D., Toumi, A., Medouakh, S. et al. A novel visual tracking method using stochastic fractal search algorithm. SIViP 15, 331–339 (2021). https://doi.org/10.1007/s11760-020-01748-7

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  • DOI: https://doi.org/10.1007/s11760-020-01748-7

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