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Visual tracking via dynamic saliency discriminative correlation filter

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Abstract

The discriminative correlation filter (DCF) is one of the crucial visual tracking methods, and it has outstanding performance. Nevertheless, DCF-based methods have an unavoidable boundary effect, which results in poor tracking performance in an abrupt scene, such as fast motion or deformation. To address this problem, we propose a novel dynamic saliency discriminative correlation filter for visual tracking. In our approach, a response guided saliency map is constructed to introduce saliency information into the filter. The method effectively highlights the target by further increasing the number of positive samples to alleviate the boundary effect. We also investigate an effective multifeature integration method to extract the target feature by employing the Felzenszwalb Histograms of Oriented Gradients (fHOG) from each color space. Finally, we apply a novel update approach to prevent filter model degradation, which uses a temporal regularization term to update the filter model. Extensive experiments on the standard OTB-2015 benchmark validate that our approach achieves competitive performance compared to other state-of-the-art trackers. Moreover, we conducted an ablation study to evaluate the effectiveness of the components in our tracker.

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References

  1. Abbass MY, Kwon KC, Kim N, Abdelwahab SA, EI-Samie FEA, Khalaf AA (2020) A survey on online learning for visual tracking. Visual Comput

  2. Ansari ZA, Nigam MJ, Kumar A (2019) Accurate tracking of manoeuvring target using scale estimation and detection. Defence Sci J 69(5)

  3. Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Trans Signal Process 50(2):174–188

    Article  Google Scholar 

  4. Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr PHS (2016) Staple: Complementary learners for real-time tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1401–1409

  5. Bolme DS, Beveridge JR, Draper BA, Lui YM (2010) Visual object tracking using adaptive correlation filters. In: Proc. IEEE CVPR., pp 2544–2550

  6. Chatfield K, Simonyan K, Vedaldi A, Zisserman A (2014) Return of the devil in the details: Delving deep into convolutional nets. arXiv:1405.3531

  7. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE Computer society conference on computer vision and pattern recognition (CVPR’05), vol 1, pp 886–893

  8. Danelljan M, Häger G., Khan F, Felsberg M (2014) Accurate scale estimation for robust visual tracking. In: British machine vision conference, nottingham, september 1-5, 2014. BMVA Press, United Kingdom

  9. Danelljan M, Häger G., Khan FS, Felsberg M (2016) Discriminative scale space tracking. IEEE Trans Pattern Anal Machine Intell 39(8):1561–1575

    Article  Google Scholar 

  10. Danelljan M, Hager G, Shahbaz Khan F, Felsberg M (2015) Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp. 4310–4318

  11. Danelljan M, Robinson A, Khan FS, Felsberg M (2016) Beyond correlation filters: Learning continuous convolution operators for visual tracking. In: European conference on computer vision, pp. 472–488

  12. Danelljan M, Shahbaz Khan F, Felsberg M, Van de Weijer J (2014) Adaptive color attributes for real-time visual tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1090–1097

  13. Fan DP, Wang W, Cheng MM, Shen J (2019) Shifting more attention to video salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8554–8564

  14. Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2009) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Machine Intell 32(9):1627–1645

    Article  Google Scholar 

  15. Felzenszwalb PF, Girshick RB, McAllester D, et al. (2009) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Machine Intell 32(9):1627–1645

    Article  Google Scholar 

  16. Feng W, Han R, Guo Q, Zhu J, Wang S (2019) Dynamic saliency-aware regularization for correlation filter-based object tracking. IEEE Trans Image Process 28(7):3232–3245

    Article  MathSciNet  Google Scholar 

  17. Fu C, Huang Z, Li Y, Duan R, Lu P (2019) Boundary effect-aware visual tracking for uav with online enhanced background learning and multi-frame consensus verification. 1–8 arXiv:1908.03701

  18. Fu C, Xu J, Lin F, Guo F, Liu T, Zhang Z (2020) Object saliency-sware dual regularized correlation filter for real-time aerial tracking. IEEE Trans Geosci Remote Sensing

  19. Gao P, Ma Y, Yuan R, Xiao L, Wang F (2019) Learning cascaded siamese networks for high performance visual tracki. In: 2019 IEEE International conference on image processing (ICIP), pp. 3078–3082

  20. Gao P, Yuan R, Wang F, Xiao L, Fujita H, Zhang Y (2020) Siamese attentional keypoint network for high performance visual tracking. Knowl-based Syst 193:105448

    Article  Google Scholar 

  21. Gao P, Zhang Q, Wang F, Xiao L, Fujita H, Zhang Y (2020) Learning reinforced attentional representation for end-to-end visual tracking. Inf Sci 517:52–67

    Article  Google Scholar 

  22. Hadfield S, Bowden R, Lebeda K (2016) The visual object tracking vot2016 challenge results. Lect Notes Comput Sci 9914:777–823

    Article  Google Scholar 

  23. Henriques JF (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: European conference on computer vision, pp. 702–715

  24. Henriques JF, Caseiro R, Martins P, Batista J (2014) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Machine Intell 37(3):583–596

    Article  Google Scholar 

  25. Hester CF, Casasent D (1980) Multivariant technique for multiclass pattern recognition. Appl. Opt. 19(11):1758–1761

    Article  Google Scholar 

  26. Jiang H, Wang J, Yuan Z, Wu Y, Zheng N, Li S (2013) Salient object detection: A discriminative regional feature integration approach. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2083–2090

  27. Kiani Galoogahi H, Fagg A, Lucey S (2017) Learning background-aware correlation filters for visual tracking. In: Proceedings of the IEEE international conference on computer vision, pp. 1135–1143

  28. Kiani Galoogahi H, Sim T, Lucey S (2015) Correlation filters with limited boundaries. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4630–4638

  29. Kristan M, Leonardis A, Matas J, Felsberg M, Pflugfelder R, ˇCehovin Zajc L., Vojir T, Bhat G, Lukezic A, Eldesokey A, et al. (2018) The sixth visual object tracking vot2018 challenge results. In: Proceedings of the European conference on computer vision (ECCV), pp. 3–53

  30. Kristan M, Leonardis A, Matas J, Felsberg M, Pflugfelder R, Cehovin Zajc L, Vojir T, Hager G, Lukezic A, Eldesokey A, et al. (2017) The visual object tracking vot2017 challenge results. In: Proceedings of the IEEE international conference on computer vision workshops, pp. 1949–1972

  31. Kristan M, Matas J, Leonardis A, Felsberg M, Cehovin L, Fernandez G, Vojir T, et al. (2015) The visual object tracking vot2015 challenge results. In: Proceedings of the IEEE international conference on computer vision workshops., pp. 1–23

  32. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp. 1097–1105

  33. Kumar BV, Mahalanobis A, Song S, Sims SRF, Epperson JF (1992) Minimum squared error synthetic discriminant functions. Opt Eng 31(5):915–922

    Article  Google Scholar 

  34. Li Y, Zhu J (2014) A scale adaptive kernel correlation filter tracker with feature integration. In: European conference on computer vision, pp. 254–265

  35. Liu K, Cai H, Wang B, Chen G, Wang X (2019) A target tracking algorithm based on multi-feature fusion. In: 2019 12th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), pp. 1–5

  36. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2):91–110

    Article  Google Scholar 

  37. Lu X, Ni B, Ma C, Yang X (2019) Learning transform-aware attentive network for object tracking. Neurocomputing 349:133–144

    Article  Google Scholar 

  38. Ma H, Acton ST, Lin Z (2020) Situp: Scale invariant tracking using average peak-to-correlation energy. IEEE Trans Image Process 29:3546–3557

    Article  Google Scholar 

  39. Peker K, Altılar D.T. (2020) Mbt: A context aware mission based tracker. In: 2020 IEEE International conference on informatics, iot, and enabling technologies (ICIot), pp. 61–66

  40. Ren Z, Gao S, Chia LT, Tsang IWH (2013) Region-based saliency detection and its application in object recognition. IEEE Trans. Circ. Syst. Video Technol. 24(5):769–779

    Article  Google Scholar 

  41. Sinopoli B, Schenato L, Franceschetti M, Poolla K, Jordan MI, Sastry SS (2004) Kalman filtering with intermittent observations. IEEE Trans Autom Control 49(9):1453–1464

    Article  MathSciNet  Google Scholar 

  42. Song H, Wang W, Zhao S, Shen J, Lam KM (2018) Pyramid dilated deeper convlstm for video salient object detection. In: Proceedings of the European conference on computer vision (ECCV), pp. 715–731

  43. Tiwari AK, Siripurapu R, Yadhunandan U (2018) Fast robust tracking via double correlation filter formulation. In: 2018 IEEE International conference on acoustics, speech and signal processing (ICASSP), pp. 1648–1652

  44. Van De Weijer J, Schmid C, Verbeek J, Larlus D (2009) Learning color names for real-world applications. IEEE Trans Image Process 18(7):1512–1523

    Article  MathSciNet  Google Scholar 

  45. Varfolomieiev A (2020) Channel-independent spatially regularized discriminative correlation filter for visual object tracking. J Real-Time Image Process. (3)

  46. Vojir T, Noskova J, Matas J (2014) Robust scale-adaptive mean-shift for tracking. Pattern Recogn Lett 49:250–258

    Article  Google Scholar 

  47. Wang C, Xu Y, Liu H, Jing X (2019) Learning correlation filters in independent feature channels for robust visual tracking. Pattern Recogn Lett 127:94–102

    Article  Google Scholar 

  48. Wang W, Shen J, Yang R, Porikli F (2017) Saliency-aware video object segmentation. IEEE Trans Pattern Anal Machine Intell 40(1):20–33

    Article  Google Scholar 

  49. Wang X, Hou Z, Yu W, Pu L, Jin Z, Qin X (2018) Robust occlusion-aware part-based visual tracking with object scale adaptation. Pattern Recogn 81:456–470

    Article  Google Scholar 

  50. Wang X, You S, Li X, Ma H (2018) Weakly-supervised semantic segmentation by iteratively mining common object features. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1354–1362

  51. Wei Y, Feng J, Liang X, Cheng MM, Zhao Y, Yan S (2017) Object region mining with adversarial erasing: a simple classification to semantic segmentation approach. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1568–1576

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

    Article  Google Scholar 

  53. Zhang D, Meng D, Zhao L, Han J (2017) Bridging saliency detection to weakly supervised object detection based on self-paced curriculum learning. arXiv:1703.01290

  54. Zhang P, Liu W, Wang D, Lei Y, Wang H, Lu H (2020) Non-rigid object tracking via deep multi-scale spatial-temporal discriminative saliency maps. Pattern Recogn. 100:107130

    Article  Google Scholar 

  55. Zhang P, Yu S, Xu J, You X, Jiang X, Jing XY, Tao D (2018) Robust visual tracking using multi-frame multi-feature joint modeling. IEEE Trans Circ Syst Video Technol 29(12):3673–3686

    Article  Google Scholar 

  56. Zhao D, Kang W, Liu G (2019) Long-term object tracking method based on dimensionality reduction. In: International conference on wireless and satellite systems, pp. 529–536

  57. Zhao D, Xiao L, Fu H, Wu T, Xu X, Dai B (2019) Augmenting cascaded correlation filters with spatial–temporal saliency for visual tracking. Inf Sci 470:78–93

    Article  Google Scholar 

  58. Zhou T, Liu F, Bhaskar H, Yang J (2017) Robust visual tracking via online discriminative and low-rank dictionary learning. IEEE Trans Cybern 48(9):2643–2655

    Article  Google Scholar 

  59. Zhu G, Wang J, Wu Y, Zhang X, Lu H (2016) Mc-hog correlation tracking with saliency proposal. In: AAAI, pp. 3690– 3696

  60. Zhu JY, Wu J, Xu Y, Chang E, Tu Z (2014) Unsupervised object class discovery via saliency-guided multiple class learning. IEEE Trans Pattern Anal Machine Intell 37(4):862–875

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61671170.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, 150001, China

    Lina Gao, Bing Liu, Ping Fu, Mingzhu Xu & Junbao Li

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  1. Lina Gao
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  2. Bing Liu
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  3. Ping Fu
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Correspondence to Ping Fu.

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Gao, L., Liu, B., Fu, P. et al. Visual tracking via dynamic saliency discriminative correlation filter. Appl Intell 52, 5897–5911 (2022). https://doi.org/10.1007/s10489-021-02260-2

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