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An extended KCF tracking algorithm based on TLD structure in low frame rate videos

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Abstract

The KCF (Kernelized Correlation Filter) algorithm achieved a good performance on target tracking challenges. However, it still has some defects and problems of false tracking in low frame rate (LFR) scenarios, target scale variation, occlusion and out of view target, that exists in the correlation filter based methods. In this paper, we overcome the shortcomings of KCF tracking algorithm based on Tracking-Learning-Detection (TLD) framework. The proposed algorithm trained two classifiers simultaneously, based on semi supervised co-training learning algorithm. Then, we comparatively evaluate the proposed method on TB-100 datasets by other trackers. The experimental results demonstrate that the precision and robustness of the improved tracking algorithm is higher than traditional KCF, TLD and the other top state-of-the-art tracking algorithms in LFR videos.

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References

  1. Bao C, Wu Y, Ling H, Ji H (2012) Real time robust l1 tracker using accelerated proximal gradient approach. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1830-1837. IEEE,

  2. Boroujeni HS, Charkari NM, Behrouzifar M (2012) Tracking multiple variable-sizes moving objects in LFR videos using a novel genetic algorithm approach. Knowl Technol, Commun Comput Inform Sci 295:143–153

    Article  Google Scholar 

  3. Cai C, Liang X, Wang B, Cui Y, Yan Y (2018) A target tracking method based on KCF for omnidirectional vision. In: 2018 37th Chinese Control Conference (CCC). IEEE, p 2674–2679

  4. Carneiro G, Nascimento JC (2011) Incremental on-line semi-supervised learning for segmenting the left ventricle of the heart from ultrasound data. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp 1700–1707

  5. Chuang MC, Hwang JN, Kuo FF, Shan MK, Williams K (2014) Recognizing live fish species by hierarchical partial classification based on the exponential benefit. Proc. IEEE Int, Conf. on Image Process., Oct

  6. Dai W, Chang T, Su K, Wang Q (2016) Improved TLD target algorithm based on feature fusion. 2nd Workshop on Advanced Research and Technology in Industry Applications

  7. Di Caterina G, Soraghan JJ (2011) An improved mean shift tracker with fast failure recovery strategy after complete occlusion. 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance.

  8. Everingham M, Gool L, Williams C, Winn J, Zisserman A (2010) The Pascal visual object class (voc) challenge. Int J Comput Vis 88(2):303–338

    Article  Google Scholar 

  9. Godec M, Roth PM, Bischof H (2012) Hough-based tracking of non-rigid objects. Proc. IEEE Int’l Conf. Computer Vision

  10. Henriques JF, Caseiro R, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: European conference on computer vision. Springer, Berlin, pp 702–715

    Google Scholar 

  11. Henriques JF, Caseiro R, Martins P, Batista J (2015) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583–596

    Article  Google Scholar 

  12. Hu J, Cai M, Li J (2017) An improved TLD method based on color feature. In: Control and Decision Conference (CCDC), 2017 29th Chinese. IEEE, pp 6096–6101

  13. Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learning-detection. In: Pattern Analysis and Machine Intelligence (PAMI)

    Google Scholar 

  14. Kwon J, Kim K, Cho K (2017) Multi-target tracking by enhancing the kernelised correlation filter-based tracker. Electron Lett 53(20):1358–1360

    Article  Google Scholar 

  15. Lee G, Mallipeddi R, Lee M (2017) Trajectory-based vehicle tracking at low frame rates. Expert Syst Appl 80:46–57

    Article  Google Scholar 

  16. Li T, Zhao WJ, Yang S, Li C (2016) An improved TLD object tracking algorithm. In: Eighth International Conference on Digital Image Processing (ICDIP 2016), vol. 10033. International Society for Optics and Photonics, p 100330H

  17. Liu P, Liu C, Zhao W, Tang X (2017) Extended kernelized correlation tracking with target enhancement and sample selection. In: Tools with Artificial Intelligence (ICTAI), 2017 IEEE 29th International Conference on. IEEE, pp 559–565

  18. Liu Y, Wang Q, Hu H, He Y (2018) A novel Real-time moving target tracking and path planning system for a Quadrotor UAV in unknown unstructured outdoor scenes. IEEE Trans Syst Man Cybern Syst 99:1–11

    Google Scholar 

  19. Miao F, XING CJ (2017) Improved TLD target tracking method based on frame difference. Electron Des Eng 7

  20. Mo Z, Ni J, Shi P, Fan X (2018) An improved tracking method based on Kernelized correlation filter with a union feature. Int J Innov Comput Inf Control 14(4):1239–1252

    Google Scholar 

  21. Ni J, Zhang X, Shi P, Zhu J (2018) An improved kernelized correlation filter based visual tracking method. Math Probl Eng

  22. Oron S, Bar-Hillel A, Levi D, Avidan S (2015) Locally orderless tracking. Int J Comput Vis 111(2):213–228

    Article  MathSciNet  Google Scholar 

  23. Palaniappan K, Bunyak F, Kumar P, Ersoy I, Jaeger S, Ganguli K, Haridas A, Fraser J, Rao RM, Seetharaman G (2010) Efficient feature extraction and likelihood fusion for vehicle tracking in low frame rate airborne video. In: Information Fusion (FUSION), 13th Conference on…., .

    Google Scholar 

  24. Pang Y, Shenb D, Chen G, Liang P, Pham K, Blasch E, Wang ZH, Ling H (2013) Low frame rate video target localization and tracking testbed. In: Proc. SPIE 8742, Ground/Air Multi sensor Interoperability, Integration, and Networking for Persistent ISR IV

    Google Scholar 

  25. Pei M, Li W, Ke Z, Gao Q (2016) Improved kernelized correlation filters tracking algorithm with adaptive learning factor. In: Control Conference (CCC), 2016 35th Chinese (pp. 4009–4013). IEEE

  26. Siqueira DL, Machado AMC (2016) People detection and tracking in low frame-rate dynamic scenes. IEEE Lat Am Trans 14(4):1966–1971

    Article  Google Scholar 

  27. Song Z, Cong Z, Yanan Z, Yuren D (2017) An improved TLD target tracking algorithm based on Mean Shift. In: 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI). IEEE, pp 387–391

  28. Wu D, Peng L (2018) Scale adaptive kernel correlation filter tracking algorithm combined with learning rate adjustment. In: MATEC Web of Conferences (Vol. 232, p. 03016). EDP Sciences

  29. Y. Wu, B. Shen and H. Ling, 2012 "Online robust image alignment via iterative convex optimization", In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1808-1814. IEEE

  30. Wu Y, Lim J, Yang MH (2015) Object tracking benchmark. IEEE Trans Pattern Anal Mach Intell 37(9):1834–1848

    Article  Google Scholar 

  31. Wu W, Wang D, Luo X, Su Y, Tian W (2018) An improved KCF tracking algorithm based on multi-feature and multi-scale. In: MIPPR 2017: Automatic Target Recognition and Navigation, vol 10608. International Society for Optics and Photonics, p 1060803

  32. Xie X, Wu F, Liu Q (2018) Object tracking based on KCF and sparse prototypes. In: Proceedings of the 2018 International Conference on Control and Computer Vision. ACM, pp 69–74

  33. Xu T, Huang C, He Q, Guan G, Zhang Y (2016) An improved TLD target tracking algorithm. In: 2016 IEEE International Conference on Information and Automation (ICIA). IEEE, pp 2051–2055

  34. Yu L, Zheng T, Shi Q (2016) Image Tracking Algorithm Improvement Based on TLD Frame. J Sign Process Image Process Pattern Recogn 9(5):431–440

    Google Scholar 

  35. Zhang X, Hu W, Xie N, Bao H, Maybank S (2015) A robust tracking system for low frame rate video. Int J Comput Vis 115(3):279–304

    Article  MathSciNet  Google Scholar 

  36. Zhang Y, Zeng C, Liang H, Luo J, Xu F (2016) A visual target tracking algorithm based on improved kernelized correlation filters. In: Mechatronics and Automation (ICMA), 2016 IEEE International Conference on. IEEE, pp 199–204

  37. Zhang J, Wang A, Wang M, Iwahori Y (2017) A novel target algorithm based on TLD combining with SLBP. Int J Perform Eng 13(4)

  38. Zhang K, Zhang L, Yang MH Real-time compressive tracking. In: European conference on computer vision. Springer, Berlin, pp 864–877

  39. L. Zhao, Y. Chen and Q. Ye, (2017)"An improved TLD algorithm based on Kalman filter and SURF feature matching", In AIP Conference Proceedings, vol. 1839, no. 1, p. 020214. AIP Publishing

  40. Zhong W, Lu H, Yang MH (2012) Robust object tracking via sparsity-based collaborative model. In: 2012 IEEE Conference on Computer vision and pattern recognition (CVPR). IEEE, pp 1838–1845

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

    Hooman Moridvaisi, Farbod Razzazi, Mohammad Ali Pourmina & Massoud Dousti

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  1. Hooman Moridvaisi
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  2. Farbod Razzazi
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  3. Mohammad Ali Pourmina
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  4. Massoud Dousti
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Correspondence to Farbod Razzazi.

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Moridvaisi, H., Razzazi, F., Pourmina, M.A. et al. An extended KCF tracking algorithm based on TLD structure in low frame rate videos. Multimed Tools Appl 79, 20995–21012 (2020). https://doi.org/10.1007/s11042-020-08867-w

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