Open Access iconOpen Access



Enhancing the Robustness of Visual Object Tracking via Style Transfer

Abdollah Amirkhani1,*, Amir Hossein Barshooi1, Amir Ebrahimi2

1 School of Automotive Engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran
2 School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, 2308, Australia

* Corresponding Author: Abdollah Amirkhani. Email: email

(This article belongs to the Special Issue: Machine Learning Applications in Medical, Finance, Education and Cyber Security)

Computers, Materials & Continua 2022, 70(1), 981-997.


The performance and accuracy of computer vision systems are affected by noise in different forms. Although numerous solutions and algorithms have been presented for dealing with every type of noise, a comprehensive technique that can cover all the diverse noises and mitigate their damaging effects on the performance and precision of various systems is still missing. In this paper, we have focused on the stability and robustness of one computer vision branch (i.e., visual object tracking). We have demonstrated that, without imposing a heavy computational load on a model or changing its algorithms, the drop in the performance and accuracy of a system when it is exposed to an unseen noise-laden test dataset can be prevented by simply applying the style transfer technique on the train dataset and training the model with a combination of these and the original untrained data. To verify our proposed approach, it is applied on a generic object tracker by using regression networks. This method’s validity is confirmed by testing it on an exclusive benchmark comprising 50 image sequences, with each sequence containing 15 types of noise at five different intensity levels. The OPE curves obtained show a 40% increase in the robustness of the proposed object tracker against noise, compared to the other trackers considered.


Cite This Article

APA Style
Amirkhani, A., Barshooi, A.H., Ebrahimi, A. (2022). Enhancing the robustness of visual object tracking via style transfer. Computers, Materials & Continua, 70(1), 981-997.
Vancouver Style
Amirkhani A, Barshooi AH, Ebrahimi A. Enhancing the robustness of visual object tracking via style transfer. Comput Mater Contin. 2022;70(1):981-997
IEEE Style
A. Amirkhani, A.H. Barshooi, and A. Ebrahimi "Enhancing the Robustness of Visual Object Tracking via Style Transfer," Comput. Mater. Contin., vol. 70, no. 1, pp. 981-997. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 2397


  • 1500


  • 3


Share Link