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A visual tracking algorithm via confidence-based multi-feature correlation filtering

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Abstract

Object tracking is an important issue in many practical computer vision applications, such as video surveillance, self-driving,and social scene understanding. Although the traditional correlation filter has been achieved the great performance in tracking accuracy and speed in a specific scenario, there are still some defects, such as weak robustness of trackers caused by using the single feature, boundary effects due to the circular shift and model corruption produced by the model update. To address the above problems, a visual tracking algorithm via confidence-based multi-feature correlation filtering is proposed in this paper. It adaptively selects histogram of oriented gradient (HOG) features or fusion features according to the confidence to improve the robustness and speed of target tracking. Firstly, a confidence level is proposed to evaluate the reliability of HOG feature based on the response map of the HOG feature. Secondly, a selective multi-feature fusion method is proposed to improve the robustness of the tracking algorithm. Thirdly, a novel model-updating mechanism, called model rollback mechanism, is proposed to reduce the impact of the model corruption. The algorithm is evaluated on the public datasets and compared with several state-of-the-art algorithms. Experimental results show that the proposed algorithm can effectively improve the performance in tracking accuracy of tracker in the above problems and is superior to the state-of-the-art tracking algorithms.

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References

  1. Alahi A, Goel K, Ramanathan V, Robicquet A, Li FF, Savarese S (2016) Social lstm: Human trajectory prediction in crowded spaces. In: Proceedings of 2016 IEEE conference on computer vision and pattern recognition, pp 961–971, Las Vegas, NV, USA

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

  3. Chen K, Tao W (2019) Learning linear regression via single-convolutional layer for visual object tracking. IEEE Trans Multimed 21(1):86–97

    Article  MathSciNet  Google Scholar 

  4. Chen Y, Fang S, Li Z (2019) Real-time visual tracking via weighted multi-feature fusion on an appearance model. J Image Graph 24(2):0291–0301

    Google Scholar 

  5. Dai K, Wang D, Lu H, Sun C, Li J (2019) Visual tracking via adaptive spatially-regularized correlation filters. In: Proceedings of 2019 IEEE/CVF conference on computer vision and pattern recognition, pp 4665–4674, Long Beach, CA, USA

  6. Dai P, Wang X, Zhang W, Chen J (2019) Instance segmentation enabled hybrid data association and discriminative hashing for online multi-object tracking. IEEE Trans Multimed 21(7):1709–1723

    Article  Google Scholar 

  7. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of 2005 IEEE computer society conference on computer vision and pattern recognition, pp 1–8, San Diego, CA, USA

  8. Danelljan M, Bhat G, Khan FS, Felsberg M (2017) Eco: efficient convolution operators for tracking. In: Proceedings of 2017 IEEE conference on computer vision and pattern recognition, pp 6931–6939, Honolulu, HI, USA

  9. Danelljan M, Hager G, Khan FS, Felsberg M (2014) Accurate scale estimation for robust visual tracking. In: Proceedings of the british machine vision conference 2014, pp 1–11, Nottingham, UK

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

  11. Danelljan M, Hager G, Khan FS, Felsberg M (2017) Discriminative scale space tracking. IEEE Trans Pattern Anal Mach Intell 39(8):1561–1575

    Article  Google Scholar 

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

  13. Dong X, Shen J, Yu D, Wang W, Liu J, Huang H (2017) Occlusion-aware real-time object tracking. IEEE Trans Multimed 19(4):763–771

    Article  Google Scholar 

  14. Du B, Sun Y, Cai S, Wu C, Du Q (2018) Object tracking in satellite videos by fusing the kernel correlation filter and the three-frame-difference algorithm. IEEE Trans Geosci Remote Sens 15(2):168–172

    Google Scholar 

  15. Fan DP, Wang W, Cheng MM, Shen J (2019) Shifting more attention to video salient object detection. In: Proceedings of 2019 IEEE/CVF conference on computer vision and pattern recognition, pp 8546–8556, Long Beach, CA, USA

  16. Galoogahi HK, Fagg A, Lucey S (2017) Learning background-aware correlation filters for visual tracking. In: Proceedings of 2017 international conference on computer vision, pp 1144–1152, Venice, Italy

  17. Galoogahi HK, Sim T, Lucey S (2015) Correlation filters with limited boundaries. In: Proceedings of 2015 IEEE conference on computer vision and pattern recognition, pp 4630–4638, Boston, MA, USA

  18. Henriques JF, Caseiro R, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceedings of 2012 european conference on computer vision, pp 702–715, Firenze, Italy

  19. 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 

  20. Huang Y, Zhao Z, Wu B, Mei Z, Cui Z, Gao G (2019) Visual object tracking with discriminative correlation filtering and hybrid color feature. Multimed Tools Appl 78:34725–34744

    Article  Google Scholar 

  21. Jia X, Lu H, Yang MH (2012) Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of 2012 IEEE Conference on computer vision and pattern recognition, pp 1822–1829, Providence, RI, USA

  22. Kwon J, Lee KM (2010) Visual tracking decomposition. In: Proceedings of 2010 IEEE Conference on computer vision and pattern recognition, pp 1269–1276, San Francisco, CA, USA

  23. Li B, Yan J, Wu W, Zhu Z, Hu X (2018) High performance visual tracking with siamese region proposal network. In: Proceedings of 2018 IEEE Conference on computer vision and pattern recognition, pp 8971–8980, Salt Lake City, UT, USA

  24. Li Y, Fu C, Ding F, Huang Z, Lu G (2020) Autotrack: Towards high-performance visual tracking for uav with automatic spatio-temporal regularization. In: Proceedings of 2020 IEEE/CVF conference on computer vision and pattern recognition, 11920–11929, Seattle, WA, USA

  25. Li Y, Zhu J (2014) A scale adaptive kernel correlation filter tracker with feature integration. In: Proceedings of 2014 european conference on computer vision, pp 254–265, Zurich, Switzerland

  26. Liang N, Wu G, Kang W, Wang Z, Feng DD (2018) Real-time long-term tracking with prediction-detection-correction. IEEE Trans Multimed 20 (9):2289–2302

    Article  Google Scholar 

  27. Liu F, Gong C, Huang X, Zhou T, Yang J, Tao D (2018) Robust visual tracking revisited: From correlation filter to template matching. IEEE Trans Image Process 27(6):2777–2790

    Article  MathSciNet  Google Scholar 

  28. Ma C, Miao Z, Zhang XP, Li M (2017) A saliency prior context model for real-time object tracking. IEEE Trans Multimed 19(11):2415–2424

    Article  Google Scholar 

  29. Mueller M, Smith N, Ghanem B (2017) Context-aware correlation filter tracking. In: Proceedings of 2017 IEEE conference on computer vision and pattern recognition, pp 1387–1395, Honolulu, HI, USA

  30. Possegger H, Mauthner T, Bischof H (2015) In defense of color-based model-free tracking. In: Proceedings of 2015 IEEE conference on computer vision and pattern recognition, 2113–2120, Boston, MA, USA

  31. Ross DA, Lim J, Lin RS, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1-3):125–141

    Article  Google Scholar 

  32. Wang J, Liu W, Xing W, Zhang S (2018) Visual object tracking with multi-scale superpixels and color-feature guided kernelized correlation filters. Signal Process Image Commun 63(4):44–62

    Article  Google Scholar 

  33. Wang M, Liu Y, Huang Z (2017) Large margin object tracking with circulant feature maps. In: Proceedings of 2017 IEEE conference on computer vision and pattern recognition, pp 4800–4808, Honolulu, HI, USA

  34. Wang N, Zhou W, Li H (2019) Reliable re-detection for long-term tracking. IEEE Trans Circ Syst Video Technol 29(3):730–743

    Article  Google Scholar 

  35. Wang N, Zhou W, Tian Q, Hong R, Wang M, Li H (2018) Multi-cue correlation filters for robust visual tracking. In: Proceedings of 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 4844–4853, Salt Lake City, UT, USA

  36. Wang Q, Teng Z, Xing J, Gao J, Hu W, Maybank S (2018) Learning attentions: Residual attentional siamese network for high performance online visual tracking. In: Proceedings of 2018 IEEE conference on computer vision and pattern recognition, pp 4854–4863, Salt Lake City, UT, USA

  37. Wang Q, Yuan C, Wang J, Zeng W (2019) Learning attentional recurrent neural network for visual tracking. IEEE Trans Multimed 21(4):930–942

    Article  Google Scholar 

  38. Wang Y, Luo X, Ding L, Wu J, Fu S (2019) Robust visual tracking via a hybrid correlation filter, vol 78

  39. Wang Z, Yoon S, Xie SJ, Lu Y, Park DS (2016) Visual tracking with semi-supervised online weighted multiple instance learning. Vis Comput 32(3):307–320

    Article  Google Scholar 

  40. Wu Y, Lim J, Yang MH (2013) Online object tracking: A benchmark. In: Proceedings of 2013 IEEE conference on computer vision and pattern recognition, pp 2411–2418, Portland, OR, USA

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

    Article  Google Scholar 

  42. Xiao Y, Li J, Chang J, Zhou Y, Zhang W (2018) Correlation filter tracking with multiscale spatial view. In: Proceedings of 2018 international joint conference on neural networks, pp 8–13, Rio de Janeiro, Brazil

  43. Yuan D, Zhang X, Liu J, Li D (2019) A multiple feature fused model for visual object tracking via correlation filters, vol 78

  44. Zhao J, Liu JJ, Fan DP, Cao Y, Yang J, Cheng MM (2019) Egnet: Edge guidance network for salient object detection. In: Proceedings of 2019 IEEE/CVF conference on computer vision, pp 8778–8787, Seoul, Korea (South)

  45. Zhao JX, Cao Y, Fan DP, Cheng MM, Li XY, Zhang L (2019) Contrast prior and fluid pyramid integration for rgbd salient object detection. In: Proceedings of 2019 IEEE/CVF Conference on computer vision and pattern recognition, pp 3922–3931, Long Beach, CA, USA

  46. Zhong W, Lu H, Yang MH (2012) Robust object tracking via sparsity-based collaborative model. In: Proceedings of 2012 IEEE conference on computer vision and pattern recognition, pp 1838–1845, Providence, RI, USA

  47. Zhou H, Ouyang W, Cheng J, Wang X, Li H (2019) Deep continuous conditional random fields with asymmetric inter-object constraints for online multi-object tracking. IEEE Trans Circ Syst Video Technol 29(4):1011–1022

    Article  Google Scholar 

  48. Zhou T, Bhaskar H, Liu F, Yang J (2017) Graph regularized and locality-constrained coding for robust visual tracking. IEEE Trans Circ Syst Video Technol 27(10):2153–2164

    Article  Google Scholar 

  49. Zhou T, Liu F, Bhaskar H, Yang J (2018) Robust visual tracking via online discriminative and low-rank dictionary learning. IEEE Trans Cybern 48 (9):2643–2655

    Article  Google Scholar 

  50. Zhou Y, Wang T, Hu R, Su H, Liu Y, Liu X, Suo J, Snoussi H (2019) Multiple kernelized correlation filters (mkcf) for extended object tracking using x-band marine radar data. IEEE Trans Signal Process 67(14):3676–3688

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Shandong University of Science and Technology, Qingdao, 266590, China

    Sheng Fang, Yichen Ma, Zhe Li & Bin Zhang

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  1. Sheng Fang
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  2. Yichen Ma
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  3. Zhe Li
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  4. Bin Zhang
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Correspondence to Zhe Li.

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This work is partially supported by the National Natural Science Foundation of China (No. 61502278), the National Key R&D Program of China (No. 2018YFC0831002), the Key R&D Program of Shandong Province (No. 2018GGX101045), the Natural Science Foundation of Shandong Province (No. ZR2020MF132,ZR2018BF014).

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Fang, S., Ma, Y., Li, Z. et al. A visual tracking algorithm via confidence-based multi-feature correlation filtering. Multimed Tools Appl 80, 23963–23982 (2021). https://doi.org/10.1007/s11042-021-10804-4

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