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Correlation tracking with implicitly extending search region

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Abstract

Recently, the correlation filters have been successfully applied to visual tracking, but the boundary effect severely limits their tracking performance. In this paper, to overcome this problem, we propose a correlation tracking framework with the capacity implicitly to extend the search region (TESR), while inhibiting the undesirable impact of the background noise. The proposed tracking method is a two-stage detection framework. The search region of the correlation tracker is extended by considering other four search centers, in addition to the target location in the previous frame. Thus our TESR will generate five potential object loactions. Then, an SVM classifier is used to determine the correct target position. We also introduce and apply the salient object detection score to regularize the output of the SVM classifier to improve its performance. The experimental results demonstrate that TESR exhibits superior performance in comparison with the state-of-the-art trackers.

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References

  1. Atkinson, R.C., Shiffrin, R.M.: Human memory: a proposed system and its control processes. Psychol. Learn. Motiv. 2, 89–195 (1968)

    Article  Google Scholar 

  2. Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.H.: Staple: complementary learners for real-time tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1401–1409 (2016)

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

  4. Bibi, A., Mueller, M., Ghanem, B.: Target response adaptation for correlation filter tracking. In: European Conference on Computer Vision, pp. 419–433. Springer (2016)

  5. Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2544–2550. IEEE (2010)

  6. Bolme, D.S., Draper, B.A., Beveridge, J.R.: Average of synthetic exact filters. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp. 2105–2112. IEEE (2009)

  7. Chen, Z., Hong, Z., Tao, D.: An experimental survey on correlation filter-based tracking. arXiv:1509.05520 (2015)

  8. Choi, J., Jin Chang, H., Jeong, J., Demiris, Y., Young Choi, J.: Visual tracking using attention-modulated disintegration and integration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4321–4330 (2016)

  9. Danelljan, M., Häger, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference, Nottingham, September 1–5, 2014. BMVA Press (2014)

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

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

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

  13. Guan, H., Cheng, B.: How do deep convolutional features affect tracking performance: an experimental study. Vis. Comput. 34(12), 1701–1711 (2018)

    Article  Google Scholar 

  14. Hare, S., Golodetz, S., Saffari, A., Vineet, V., Cheng, M.M., Hicks, S.L., Torr, P.H.: Struck: structured output tracking with kernels. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2096–2109 (2016)

    Article  Google Scholar 

  15. He, A., Luo, C., Tian, X., Zeng, W.: A twofold siamese network for real-time object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4834–4843 (2018)

  16. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: European Conference on Computer Vision, pp. 702–715. Springer (2012)

  17. Henriques, J.F., Rui, C., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)

    Article  Google Scholar 

  18. Hong, Z., Chen, Z., Wang, C., Mei, X., Prokhorov, D., Tao, D.: Multi-store tracker (muster): a cognitive psychology inspired approach to object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 749–758 (2015)

  19. Kiani Galoogahi, H., Fagg, A., Lucey, S.: Learning background-aware correlation filters for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1135–1143 (2017)

  20. Kiani Galoogahi, H., Sim, T., Lucey, S.: Correlation filters with limited boundaries. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4630–4638 (2015)

  21. Kim, H.U., Lee, D.Y., Sim, J.Y., Kim, C.S.: Sowp: spatially ordered and weighted patch descriptor for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3011–3019 (2015)

  22. Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R., Čehovin, L., Vojs̃, T., Häger, G., Lukežič, A., Fernndez, G.: The visual object tracking vot2016 challenge results. In: European Conference on Computer Vision (2016)

  23. Kristan, M., Pflugfelder, R., Leonardis, A., Matas, J., Porikli, F., Čehovin, L., Nebehay, G., Fernandez, G., Vojs̃, T., Gatt, A.: The visual object tracking vot2014 challenge results. In: European Conference on Computer Vision (2014)

  24. Li, B., Yan, J., Wu, W., Zhu, Z., Hu, X.: High performance visual tracking with siamese region proposal network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8971–8980 (2018)

  25. Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. ECCV Workshops 2, 254–265 (2014)

    Google Scholar 

  26. Li, Y., Zhu, J., Hoi, S.C.: Reliable patch trackers: robust visual tracking by exploiting reliable patches. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 353–361 (2015)

  27. Liu, T., Wang, G., Yang, Q.: Real-time part-based visual tracking via adaptive correlation filters. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4902–4912 (2015)

  28. Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.Y.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011)

    Article  Google Scholar 

  29. Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Hierarchical convolutional features for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3074–3082 (2015)

  30. Mbelwa, J.T., Zhao, Q., Lu, Y., Liu, H., Wang, F., Mbise, M.: Objectness-based smoothing stochastic sampling and coherence approximate nearest neighbor for visual tracking. Vis. Comput. 35(3), 371–384 (2019)

    Article  Google Scholar 

  31. Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4293–4302 (2016)

  32. Ning, J., Yang, J., Jiang, S., Zhang, L., Yang, M.H.: Object tracking via dual linear structured SVM and explicit feature map. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4266–4274 (2016)

  33. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  34. Smeulders, A.W., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: an experimental survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1442–1468 (2014)

    Article  Google Scholar 

  35. Song, Y., Ma, C., Gong, L., Zhang, J., Lau, R.W., Yang, M.H.: Crest: convolutional residual learning for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2555–2564 (2017)

  36. Sui, Y., Zhang, Z., Wang, G., Tang, Y., Zhang, L.: Real-time visual tracking: promoting the robustness of correlation filter learning. In: European Conference on Computer Vision, pp. 662–678. Springer (2016)

  37. Wang, N., Shi, J., Yeung, D.Y., Jia, J.: Understanding and diagnosing visual tracking systems. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3101–3109 (2015)

  38. Wang, Q., Teng, Z., Xing, J., Gao, J., Hu, W., Maybank, S.: Learning attentions: residual attentional siamese network for high performance online visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4854–4863 (2018)

  39. Wang, Q., Zhang, L., Bertinetto, L., Hu, W., Torr, P.H.: Fast online object tracking and segmentation: A unifying approach. arXiv:1812.05050 (2018)

  40. Wang, Q., Zhang, M., Xing, J., Gao, J., Hu, W., Maybank, S.: Do not lose the details: reinforced representation learning for high performance visual tracking. In: 27th International Joint Conference on Artificial Intelligence (2018)

  41. Wang, X., Jabri, A., Efros, A.A.: Learning correspondence from the cycle-consistency of time. arXiv:1903.07593 (2019)

  42. Wang, Y., Hu, S., Wu, S.: Object tracking based on huber loss function. Vis. Comput. 35(11), 1641–1654 (2019)

    Article  Google Scholar 

  43. Wang, Z., Vucetic, S.: Online training on a budget of support vector machines using twin prototypes. Stat. Anal. Data Min. ASA Data Sci. J. 3(3), 149–169 (2010)

    Article  MathSciNet  Google Scholar 

  44. Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411–2418 (2013)

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

    Article  Google Scholar 

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

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

  48. Zhang, T., Bibi, A., Ghanem, B.: In defense of sparse tracking: circulant sparse tracker. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3880–3888 (2016)

  49. Zhu, Z., Wang, Q., Li, B., Wu, W., Yan, J., Hu, W.: Distractor-aware siamese networks for visual object tracking. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 101–117 (2018)

  50. Zuo, W., Wu, X., Lin, L., Zhang, L., Yang, M.H.: Learning support correlation filters for visual tracking. arXiv:1601.06032 (2016)

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Funding

This study was funded by the 111 Project of Chinese Ministry of Education under Grant B12018 and the National Natural Science Foundation of China under Grant 61373055; 61672265.

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Authors and Affiliations

  1. School of IoT Engineering, Jiangnan University, Wuxi, China

    Qiang Qian, Xiao-Jun Wu & Tian-Yang Xu

  2. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, China

    Qiang Qian

  3. Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK

    Josef Kittler

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  1. Qiang Qian
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Correspondence to Xiao-Jun Wu.

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Qian, Q., Wu, XJ., Kittler, J. et al. Correlation tracking with implicitly extending search region. Vis Comput 37, 1029–1043 (2021). https://doi.org/10.1007/s00371-020-01850-4

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