Asian Journal of Mathematics and Computer Research

Target Tracking Based on Radon Transform Data Appearance Modeling

Asian Journal of Mathematics and Computer Research, Volume 30, Issue 3, Page 10-18
DOI: 10.56557/ajomcor/2023/v30i38309

Abstract


This article mainly focuses on an important challenge in target tracking in complex environments the real-time performance of algorithm operation. A new target appearance model based on Radon transform data is studied, and it is introduced into the correlation filtering framework for filtering template training. A fast tracking algorithm and target scale update scheme based on correlation filtering are proposed. The experimental results show that the tracking algorithm proposed in this paper has better robustness and real-time performance compared to current mainstream tracking algorithms, providing a new technical approach for research related to object detection and tracking. The tracking algorithm proposed in this article can also be seen as a framework, where the projected object can not only be the grayscale of the original pixel, but also include multi-channel color values, HOG, and other attributes.

Keywords:
  • Target tracking
  • radon transform
  • appearance modeling
  • correlation filtering

How to Cite

Yang , L. (2023). Target Tracking Based on Radon Transform Data Appearance Modeling. Asian Journal of Mathematics and Computer Research, 30(3), 10–18. https://doi.org/10.56557/ajomcor/2023/v30i38309

References

Bolme DS, Beveridge JR, Draper BA, et al. Visual object tracking using adaptive correlation filters[C]// Computer Vision and Pattern Recognition. IEEE. 2010:2544-2550.

Rahmat B, Waluyo M, Rachmanto TA, et al. Video-based tancho koi fish tracking system using CSK, DFT, and LOT [J]. Journal of Physics Conference Series. 2020;1569:022036. DOI:10.1088/1742-6596/1569/2/022036.

Huang B, Xu T, Jiang S, et al. Robust visual tracking via constrained multi-kernel correlation filters [J]. IEEE Transactions on Multimedia. 2020;22(11):2820-2832.

Xingshuo J, Weijun Z, Ting X, et al. Adaptive complementary learners with diversified color attributes for object tracking [J]. Journal of Computer-Aided Design & Computer Graphics; 2018. DOI:10.3724/SP.J.1089.2018.17207

Safaei N, Smadi O, Safaei B, et al. Efficient road crack detection based on an adaptive pixel-level segmentation algorithm [J]. Transportation Research Record. 2021;2675(9):370-381.

Zhong JL, Gan YF, Vong CM, et al. Effective and efficient pixel-level detection for diverse video copy-move forgery types [J]. Pattern Recognition. 2021;122(2): 82-86.

Zhang WF, He QS, Liang HH. Scale-adaptive block kernel correlation filtering target tracking algorithm [J]. Journal of Taiyuan University of Science and Technology. 2022;(2): 8-14.

Sato M, Kimura Y, Masuta J, et al. Improvement of frequency resolution using sub-binstructure in discrete Fourier transform [J]. Applied Optics. 2021;60(21):6290-6301.

Zhang K, Zhang L, Yang MH, et al. Fast tracking via spatio-temporal context learning[J]. Computer Science; 2013.

Danelljan M, Häger G, Khan FS, et al. Accurate scale estimation for robust visual tracking[C]// British Machine Vision Conference. 2014;65:1-65.

Li Y, Zhu J. A scale adaptive kernel correlation filter tracker with feature integration [C] // European Conference on Computer Vision.Springer, Cham; 2014. DOI: 10.1007/978-3-319-16181-5_18

Danelljan M, Hager G, Khan FS, et al. Learning spatially regularized correlation filters for visual tracking[C]// IEEE International Conference on Computer Vision. IEEE Computer Society. 2015:4310-4318.

  • 0 times
    PDF Download: 0 times

Download Statistics

Downloads

Download data is not yet available.

Current Issue

Subscription

Login to access subscriber-only resources.

Information