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Object Tracking with Multiple Dynamic Templates Updating

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Image and Vision Computing (IVCNZ 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13836))

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Abstract

Most existing Siamese visual trackers see the object tracking task as similarity learning between a search image and a single template image. Utilizing only one template leads to the negligence of the rich semantic information in other frames. Meanwhile, those Siamese trackers with temporal context exploitation either incorporate specially designed non-generic modules or include online-learning parts which compromise real-time performance. In this paper, we propose a novel model architecture incorporating multiple dynamic templates in a Siamese visual tracker to maximize temporal information utilization. To attain a favorable appearance representation from these templates, we propose an online dynamic template pool updater that leverages the frames with dissimilar appearances. Furthermore, we design a new hard positive sampling strategy to train the tracker with dissimilar templates. With the proposed methods, a Siamese tracker can be straightforwardly transformed and trained to benefit from the temporal correlations among frames. Comprehensive experiments on various tracking datasets show positive results and prove the effectiveness of the proposed methods.

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References

  1. Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850–865. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_56

    Chapter  Google Scholar 

  2. Cao, Z., Huang, Z., Pan, L., Zhang, S., Liu, Z., Fu, C.: TCTrack: temporal contexts for aerial tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14798–14808 (2022)

    Google Scholar 

  3. Chen, Z., Zhong, B., Li, G., Zhang, S., Ji, R.: Siamese box adaptive network for visual tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6668–6677 (2020)

    Google Scholar 

  4. Deshmukh, S.S., Joshi, C.M., Patel, R.S., Gurav, Y.: 3D object tracking and manipulation in augmented reality. Int. J. Emerg. Technol. Innov. Res. 5(1), 287–289 (2018)

    Google Scholar 

  5. Fan, H., et al.: LaSOT: a high-quality benchmark for large-scale single object tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  6. Fu, Z., Liu, Q., Fu, Z., Wang, Y.: STMTrack: template-free visual tracking with space-time memory networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13774–13783 (2021)

    Google Scholar 

  7. Guo, D., Wang, J., Cui, Y., Wang, Z., Chen, S.: SiamCAR: siamese fully convolutional classification and regression for visual tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6269–6277 (2020)

    Google Scholar 

  8. Guo, Q., Feng, W., Zhou, C., Huang, R., Wan, L., Wang, S.: Learning dynamic siamese network for visual object tracking. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1763–1771 (2017)

    Google Scholar 

  9. 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 (CVPR), pp. 4834–4843 (2018)

    Google Scholar 

  10. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)

    Google Scholar 

  11. Held, D., Thrun, S., Savarese, S.: Learning to track at 100 FPS with deep regression networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 749–765. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_45

    Chapter  Google Scholar 

  12. Huang, L., Zhao, X., Huang, K.: GOT-10k: a large high-diversity benchmark for generic object tracking in the wild. IEEE Trans. Pattern Anal. Mach. Intell. 43(5), 1562–1577 (2021)

    Article  Google Scholar 

  13. Jin, S., et al.: Unsupervised hard example mining from videos for improved object detection. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)

    Google Scholar 

  14. Kamble, P.R., Keskar, A.G., Bhurchandi, K.M.: A deep learning ball tracking system in soccer videos. Opto-Electron. Rev. 27(1), 58–69 (2019)

    Article  Google Scholar 

  15. Kristan, M., et al.: The sixth visual object tracking VOT2018 challenge results. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)

    Google Scholar 

  16. Kristan, M., et al.: The seventh visual object tracking VOT2019 challenge results. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (2019)

    Google Scholar 

  17. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C., Bottou, L., Weinberger, K. (eds.) Advances in Neural Information Processing Systems (NIPS), vol. 25. Curran Associates, Inc. (2012)

    Google Scholar 

  18. LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)

    Article  Google Scholar 

  19. Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., Yan, J.: SiamRPN++: evolution of siamese visual tracking with very deep networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4282–4291 (2019)

    Google Scholar 

  20. 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 (CVPR), pp. 8971–8980 (2018)

    Google Scholar 

  21. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  22. Mueller, M., Smith, N., Ghanem, B.: A benchmark and simulator for UAV tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 445–461. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_27

    Chapter  Google Scholar 

  23. Ning, G., et al.: Spatially supervised recurrent convolutional neural networks for visual object tracking. In: 2017 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–4 (2017)

    Google Scholar 

  24. Rangesh, A., Trivedi, M.M.: No blind spots: full-surround multi-object tracking for autonomous vehicles using cameras and lidars. IEEE Trans. Intell. Veh. 4(4), 588–599 (2019)

    Article  Google Scholar 

  25. Real, E., Shlens, J., Mazzocchi, S., Pan, X., Vanhoucke, V.: YouTube-BoundingBoxes: a large high-precision human-annotated data set for object detection in video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5296–5305 (2017)

    Google Scholar 

  26. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  27. Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 761–769 (2016)

    Google Scholar 

  28. Voigtlaender, P., Luiten, J., Torr, P.H., Leibe, B.: Siam R-CNN: visual tracking by re-detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6578–6588 (2020)

    Google Scholar 

  29. Wang, N., Zhou, W., Wang, J., Li, H.: Transformer meets tracker: exploiting temporal context for robust visual tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1571–1580 (2021)

    Google Scholar 

  30. Wang, Q., Zhang, L., Bertinetto, L., Hu, W., Torr, P.H.: Fast online object tracking and segmentation: a unifying approach. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1328–1338 (2019)

    Google Scholar 

  31. Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

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

    Article  Google Scholar 

  33. Xu, Y., Wang, Z., Li, Z., Yuan, Y., Yu, G.: SiamFC++: towards robust and accurate visual tracking with target estimation guidelines. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 07, pp. 12549–12556 (2020)

    Google Scholar 

  34. Yan, B., Peng, H., Fu, J., Wang, D., Lu, H.: Learning spatio-temporal transformer for visual tracking. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10448–10457 (2021)

    Google Scholar 

  35. Zhang, L., Gonzalez-Garcia, A., Weijer, J.V.D., Danelljan, M., Khan, F.S.: Learning the model update for Siamese trackers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)

    Google Scholar 

  36. 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) (2018)

    Google Scholar 

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Acknowledgements

This work is supported by the Chinese Scholarship Council (CSC), grant number 201907820021.

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Authors and Affiliations

  1. PSI-EAVISE Research Group, Department of Electrical Engineering, KU Leuven, Leuven, Belgium

    Mingyang Zhang, Kristof Van Beeck & Toon Goedemé

Authors
  1. Mingyang Zhang
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  2. Kristof Van Beeck
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  3. Toon Goedemé
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Correspondence to Mingyang Zhang .

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Editors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

  2. Auckland University of Technology, Auckland, New Zealand

    Minh Nguyen

  3. Auckland University of Technology, Auckland, New Zealand

    Martin Stommel

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Zhang, M., Van Beeck, K., Goedemé, T. (2023). Object Tracking with Multiple Dynamic Templates Updating. In: Yan, W.Q., Nguyen, M., Stommel, M. (eds) Image and Vision Computing. IVCNZ 2022. Lecture Notes in Computer Science, vol 13836. Springer, Cham. https://doi.org/10.1007/978-3-031-25825-1_11

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  • DOI: https://doi.org/10.1007/978-3-031-25825-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25824-4

  • Online ISBN: 978-3-031-25825-1

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