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A real-time object tracking algorithm based on dual feature model kernel correlation filtering

  • 1182: Deep Processing of Multimedia Data
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Abstract

Existing target tracking algorithms are mainly divided into two categories: (1) deep learning-based approaches that have high accuracy and low speed; (2) correlation filtering-based approaches that have high speed and low accuracy. In order to balance the real-time performance and accuracy, this paper improves the KCF algorithm. The main innovation lies in the development of an adaptive dual feature model: the main feature model uses shallow texture features (Histogram of Oriented Gradients, HOG), and the auxiliary feature model uses features from convolutional neural networks (CNNs) that contain deep semantic information. These work together in an optimal manner to produce a more stable correlation filter. In order to improve the speed of the algorithm, we use principal component analysis to reduce the dimensionality of the high-dimensional CNN features. In addition, this paper also improves the accuracy of the tracking algorithm by means of scale optimization and optimization of the solution method. Our algorithm is compared with current advanced tracking algorithms with real-time speed, such as SiamFC, MEEM, SAMF, DSST, KCF, Struck, and TLD. The OPE result using the public data set OTB-2013 shows that the algorithm in this paper ranks first in the distance precision rate. Compared with the KCF algorithm, the distance precision rate and overlap success rate are improved by 25.9% and 23.2%, respectively, and the average speed of the proposed algorithm can reach 38 FPS.

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References

  1. Babenko B, Yang M, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(8):1619–1632

  2. Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PH (2016) Fully-convolutional siamese networks for object tracking. In: European Conference on Computer Vision, pp 850–865

  3. Bolme DS, Beveridge JR, Draper BA, Lui YM (2010) Visual object tracking using adaptive correlation filters. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 2544–2550

  4. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 886–893

  5. Danelljan M, Khan FS, Felsberg M, van de Weijer J (2014) Adaptive color attributes for real-time visual tracking. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp 1090–1097

  6. Danelljan M, Hager G, Shahbaz Khan F, Felsberg M (2014) Accurate scale estimation for robust visual tracking. In: Proceedings of the British Machine Vision Conference (BMVC), 2014

  7. Danelljan M, Hager G, Shahbaz Khan F, Felsberg M (2015) Convolutional features for correlation filter based visual tracking. In: 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), pp 621–629

  8. Danelljan M, Robinson A, Shahbaz Khan F, Felsberg M (2016) Beyond correlation filters: learning continuous convolution operators for visual tracking. In: European Conference on Computer Vision, pp 472–488

  9. Deng J, Dong W, Socher R, Li L, Li K, Li F (2009) Imagenet: a large-scale hierarchical image database. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp 248–255

  10. Hare S, Saffari A, Torr PHS (2011) Struck: structured output tracking with kernels. In: 2011 International Conference on Computer Vision, pp 263–270

  11. Henriques JF, Caseiro R, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceedings of the 12th European conference on Computer Vision, pp 702–715

  12. Henriques JF, Caseiro R, Martins P, Batista J (2015) Highspeed tracking with kernelized correlation filters. TPAMI 37(3):583–596

    Article  Google Scholar 

  13. Hong S, You T, Kwak S, Han B (2015) Online tracking by learning discriminative saliency map with convolutional neural network. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning, pp 597–606

  14. Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learningdetection. TPAMI 34(7):1409–1422

    Article  Google Scholar 

  15. Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learning detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(7):1409–1422

  16. Li Y, Zhu J (2014) A scale adaptive kernel correlation filter tracker with feature integration. In: European Conference on Computer Vision, pp 254–265

  17. Li H, Li Y, Porikli F (2014) Deeptrack: learning discriminative feature representations by convolutional neural networks for visual tracking. In: Proceedings of the British Machine Vision Conference. https://doi.org/10.5244/c.28.56

  18. Nam H, Han B (2016) Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2016.465

  19. Nam H, Baek M, Han B (2016) Modeling and propagating cnns in a tree structure for visual tracking. arXiv, https://arxiv.org/abs/1608.07242

  20. Smeulders AWM, Chu DM, Cucchiara R, Calderara S, Dehghan A, Shah M (2014) Visual tracking: an experimental survey. TPAMI 36(7):1442–1468

    Article  Google Scholar 

  21. Vedaldi A, Lenc K (2015) Matconvnet – convolutional neural networks for matlab. In: Proceedings of the 23rd ACM international conference on Multimedia, pp 689–692

  22. Wang N, Yeung D (2013) Learning a deep compact image representation for visual tracking. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, pp 809–817

  23. Wang L, Liu T, Wang G, Chan KL, Yang Q (2015) Video tracking using learned hierarchical features. TIP 24(4):1424–1435

    MathSciNet  MATH  Google Scholar 

  24. Wu YJ, Yang M-H (2013) Online object tracking: a benchmark. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2013.312

  25. Zhang K, Zhang L, Yang M-H (2012) Real-time compressive tracking. In: Proceedings of the 12th European conference on Computer Vision, pp 864–877

  26. Zhang K, Zhang L, Liu Q, Zhang D, Yang M-H (2014) Fast visual tracking via dense spatio-temporal context learning. In: European Conference on Computer Vision, pp 127–141

  27. Zhang J, Ma S, Sclaroff S (2014) MEEM: robust tracking via multiple experts using entropy minimization. In: European Conference on Computer Vision, pp 188–203

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Authors and Affiliations

  1. College of Software, Northeastern University, Shenyang, 110000, China

    Beilei Wang

  2. College of Information Science and Engineering, Northeastern University, ShenYang, 110000, China

    Lu Meng & Chengxin Li

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  1. Beilei Wang
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  2. Lu Meng
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  3. Chengxin Li
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Correspondence to Lu Meng.

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Wang, B., Meng, L. & Li, C. A real-time object tracking algorithm based on dual feature model kernel correlation filtering. Multimed Tools Appl 81, 19113–19134 (2022). https://doi.org/10.1007/s11042-020-10225-9

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  • DOI: https://doi.org/10.1007/s11042-020-10225-9

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