Volume 14, Issue 4 (Journal of Control, V.14, N.4 Winter 2021)
JoC 2021, 14(4): 67-79 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Rohani M, farsi H, Zahiri mamghani S H. Moving object tracking in video by using fuzzy particle swarm optimization algorithm. JoC 2021; 14 (4) :67-79
URL: http://joc.kntu.ac.ir/article-1-672-en.html
URL: http://joc.kntu.ac.ir/article-1-672-en.html
1- University of Birjand
Abstract: (6393 Views)
Nowadays, one of the most fundamental processes for realization video of contents is the object tracking, in which the process of location the moving object is performed in each video frame. In tracking process, the target must be described by a feature. In this paper, for the purpose of describing the target and removing the appearance sensitivity, the weighted color histogram is used as a target feature in order to reduce the effect of edge pixels on the target feature. This reduces the sensitivity of the algorithm to change deformation, scale variation and rotation, as well as the occlusion on the description of target feature. In the proposed method, particle swarm optimization algorithm has been used for search process. Maximization of the similarity function and calculating the minimum Bhattacharyya distance are used to determine target location. The fuzzy control parameters are used for the particle swarm optimization algorithm, which provides a novel method, which can regulate each control parameter and update according to the different states of each particle in each generation. The improved particle swarm algorithm is evaluated with 11 benchmark functions. The obtained results by improved algorithm show that appropriate convergence in a low number of iterations. The proposed method compared to state-of-the-art methods provides high performance in the success and precision rate on the OTB50 dataset.
Keywords: Object tracking, Improved particle swarm optimization algorithm, Weighted color histogram feature.
Type of Article: Research paper |
Subject:
Special
Received: 2019/05/26 | Accepted: 2020/02/5 | ePublished ahead of print: 2020/10/5 | Published: 2021/02/19
Received: 2019/05/26 | Accepted: 2020/02/5 | ePublished ahead of print: 2020/10/5 | Published: 2021/02/19
References
1. [1] M. Swathy, P. Nirmala and P. Geethu, "Survey on vehicle detection and tracking techniques in video surveillance", International Journal of Computer Applications, vol. 160, no. 7, pp. 22-28, 2017. [DOI:10.5120/ijca2017913086]
2. [2] I. Pham and M. Polasek, "Algorithm for military object detection using image data", IEEE/AIAA 33rd Digital Avionics Systems Conference (DASC), pp. 1-15, 2014. [DOI:10.1109/DASC.2014.6979457]
3. [3] A. Yilmaz, O. Javed and M. Shah, "Object tracking: A survey", Acm computing surveys (CSUR), vol. 38, no. 4, pp. 13-20, 2006. [DOI:10.1145/1177352.1177355]
4. [4] B. Zhuang, H. Lu, Z. Xiao, and D. Wang, "Visual tracking via discriminative sparse similarity map", IEEE Transactions on Image Processing, vol. 23, no. 4, pp. 1872-1881, 2014. [DOI:10.1109/TIP.2014.2308414]
5. [5] H. Farsi, "Improvement of minimum tracking in minimum statistics noise estimation method", Signal Processing: An International Journal (SPIJ), vol. 4, no. 1, pp. 17-25, 2010.
6. [6] J. F. Henriques, R. Caseiro, P. Martins and J. Batista, "Exploiting the circulant structure of tracking-by-detection with kernels", European conference on computer vision, pp. 702-715, 2012. [DOI:10.1007/978-3-642-33765-9_50]
7. [7] J. F. Henriques, R. Caseiro, P. Martins and J. Batista, "High-speed tracking with kernelized correlation filters", IEEE Transactions on pattern analysis and machine intelligence, vol. 37, no. 3, pp. 583-596, 2015. [DOI:10.1109/TPAMI.2014.2345390]
8. [8] S. Hare et al., "Struck: Structured output tracking with kernels", IEEE Transactions on pattern analysis and machine intelligence, vol. 38, no. 10, pp. 2096-2109, 2016. [DOI:10.1109/TPAMI.2015.2509974]
9. [9] Z. Kalal, K. Mikolajczyk and J. Matas, "Tracking-learning-detection", IEEE Transactions on pattern analysis and machine intelligence, vol. 34, no. 7, pp. 1409-1422, 2012. [DOI:10.1109/TPAMI.2011.239]
10. [0] M. Danelljan, G. Häger, F. S. Khan and M. Felsberg, "Discriminative scale space tracking," IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 8, pp. 1561-1575, 2017. [DOI:10.1109/TPAMI.2016.2609928]
11. [1] K. Zhang, L. Zhang, Q. Liu, D. Zhang and M. Yang, "Fast visual tracking via dense spatio-temporal context learning", European conference on computer vision, pp. 127-141, 2014. [DOI:10.1007/978-3-319-10602-1_9]
12. [2] D. S. Bolme, J. R. Beveridge, B. A. Draper and Y. M. Lui, "Visual object tracking using adaptive correlation filters", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2544-2550, 2010. [DOI:10.1109/CVPR.2010.5539960]
13. [3] M. Danelljan, G. Häger, F. Khan and M. Felsberg, "Accurate scale estimation for robust visual tracking", British Machine Vision Conference, pp. 1-5, 2014. [DOI:10.5244/C.28.65]
14. [4] M. Gao, X. He, D. Luo, J. Jiang and Q. Teng, "Object tracking using firefly algorithm", IET Computer Vision, vol. 7, no. 4, pp. 227-237, 2013. [DOI:10.1049/iet-cvi.2012.0207]
15. [5] N. Hussain, A. Khan, S. G. Javed and M. Hussain, "Particle swarm optimization based object tracking using HOG features", IEEE 9th International Conference on Emerging Technologies (ICET), pp. 1-6, 2013. [DOI:10.1109/ICET.2013.6743516]
16. [6] F. Sha, C. Bae, G. Liu, X. Zhao, Y. Chung and W. Yeh, "A categorized particle swarm optimization for object tracking", IEEE Congress on Evolutionary Computation (CEC), pp. 2737-2744, 2015. [DOI:10.1109/CEC.2015.7257228]
17. [7] C. Bae, K. Kang, G. Liu and Y. Chung, "A novel real time video tracking framework using adaptive discrete swarm optimization", Expert Systems with Applications, vol. 64, no. 1, pp. 385-399, 2016. [DOI:10.1016/j.eswa.2016.08.027]
18. [8] J. Kennedy, "Particle swarm optimization", Encyclopedia of machine learning, pp. 760-766, 2010.
19. [9] S. Mirjalili, A. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris and S. M. Mirjalili, "Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems", Advances in Engineering Software, vol. 114, pp. 163-191, 2017. [DOI:10.1016/j.advengsoft.2017.07.002]
20. [20] S. Mirjalili and A. Lewis, "The whale optimization algorithm", Advances in engineering software, vol. 95, pp. 51-67, 2016. [DOI:10.1016/j.advengsoft.2016.01.008]
21. [21] S. Mirjalili, "SCA: a sine cosine algorithm for solving optimization problems", Knowledge-Based Systems, vol. 96, pp. 120-133, 2016. [DOI:10.1016/j.knosys.2015.12.022]
22. [22] J. Kennedy and R. Eberhart, "Particle swarm optimization (PSO)", IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942-1948, 1995.
23. [23] Y. Shi and R. Eberhart, "A modified particle swarm optimizer", IEEE international conference on evolutionary computation proceedings world congress on computational intelligence (Cat. No. 98TH8360), pp. 69-73, 1998.
24. [24] A. Chatterjee and P. Siarry, "Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization", Computers and operations research, vol. 33, no. 3, pp. 859-871, 2006. [DOI:10.1016/j.cor.2004.08.012]
25. [25] H. Zhu, C. Zheng, X. Hu and X. Li, "Adaptive PSO using random inertia weight and its application in UAV path planning", Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment, vol. 7128, p. 712814-712819, 2008. [DOI:10.1117/12.806636]
26. [26] T. Niknam, "A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem", Applied Energy, vol. 87, no. 1, pp. 327-339, 2010. [DOI:10.1016/j.apenergy.2009.05.016]
27. [27] Y. Maldonado, O. Castillo and P. Melin, "Particle swarm optimization of interval type-2 fuzzy systems for FPGA applications", Applied Soft Computing, vol. 13, no. 1, pp. 496-508, 2013. [DOI:10.1016/j.asoc.2012.08.032]
28. [28] T. Krzeszowski and K. Wiktorowicz, "Evaluation of selected fuzzy particle swarm optimization algorithms", Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 571-575, 2016. [DOI:10.15439/2016F206]
29. [29] F. Olivas, L. Amador-Angulo, J. Perez, C. Caraveo, F. Valdez and O. Castillo, "Comparative study of type-2 fuzzy particle swarm, bee colony and bat algorithms in optimization of fuzzy controllers", Algorithms, vol. 10, no. 3, p. 101-128, 2017. [DOI:10.3390/a10030101]
30. [30] F. Valdez, J. C. Vazquez and F. Gaxiola, "Fuzzy dynamic parameter adaptation in ACO and PSO for designing fuzzy controllers: the cases of water level and temperature control", Advances in Fuzzy Systems, vol. 2018, no. 1, pp. 1-19, 2018. [DOI:10.1155/2018/1274969]
31. [31] B. Borowska, "Nonlinear inertia weight in particle swarm optimization", 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), vol. 1 ,pp. 296-299, 2017. [DOI:10.1109/STC-CSIT.2017.8098790]
32. [32] C. Du, Z. Yin, Y. Zhang, J. Liu, X. Sun and Y. Zhong, "Research on active disturbance rejection control with parameter autotune mechanism for induction motors based on adaptive particle swarm optimization algorithm with dynamic inertia weight", IEEE Transactions on Power Electronics, vol. 34, no. 3, pp. 2841-2855, 2019. [DOI:10.1109/TPEL.2018.2841869]
33. [33] M. Swain and D. Ballard, "Color indexing", Computer Vision, vol. 7, no. 1, pp. 11-32, 1991. [DOI:10.1007/BF00130487]
34. [34] D. Zhang, J. Zhang and C. Xia, "Multi-complementary model for long-term tracking", Sensors, vol. 18, no. 2, pp. 527-552, 2018. [DOI:10.3390/s18020527]
35. [35] L. Luo and X. Fan, "Immune particle filter algorithm for target tracking based on histograms of color and oriented gradient", Optical Sensing and Imaging Technology and Applications, vol. 10462, pp. 104622Q, 2017.
36. [36] A. Sharma, A. Malik and R. Rohilla, "A robust mean shift integrating color, GLCM based texture features and frame differencing", International Journal of Scientific and Engineering Research, vol. 7, no. 2, pp. 1386-1398, 2016.
37. [37] Y. Wu, J. Lim and M. Yang, "Online object tracking: A benchmark", IEEE Computer vision and pattern recognition (CVPR), pp. 2411-2418, 2013. [DOI:10.1109/CVPR.2013.312]
38. [38] J. Ning, J. Yang, S. Jiang, L. Zhang and M. Yang, "Object tracking via dual linear structured SVM and explicit feature map", Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4266-4274, 2016. [DOI:10.1109/CVPR.2016.462]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
