Skip to main content
Springer Nature Link
Log in

CLNet: A Compact Latent Network for Fast Adjusting Siamese Trackers

  • Conference paper
  • First Online:
Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12365))

Included in the following conference series:

Abstract

In this paper, we provide a deep analysis for Siamese-based trackers and find that the one core reason for their failure on challenging cases can be attributed to the problem of decisive samples missing during offline training. Furthermore, we notice that the samples given in the first frame can be viewed as the decisive samples for the sequence since they contain rich sequence-specific information. To make full use of these sequence-specific samples, we propose a compact latent network to quickly adjust the tracking model to adapt to new scenes. A statistic-based compact latent feature is proposed to efficiently capture the sequence-specific information for the fast adjustment. In addition, we design a new training approach based on a diverse sample mining strategy to further improve the discrimination ability of our compact latent network. To evaluate the effectiveness of our method, we apply it to adjust a recent state-of-the-art tracker, SiamRPN++. Extensive experimental results on five recent benchmarks demonstrate that the adjusted tracker achieves promising improvement in terms of tracking accuracy, with almost the same speed. The code and models are available at https://github.com/xingpingdong/CLNet-tracking.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Andrychowicz, M., et al.: Learning to learn by gradient descent by gradient descent. In: NeurIPS (2016)

    Google Scholar 

  2. Ba, J., Hinton, G.E., Mnih, V., Leibo, J.Z., Ionescu, C.: Using fast weights to attend to the recent past. In: NeurIPS (2016)

    Google Scholar 

  3. 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 

  4. Choi, J., Kwon, J., Lee, K.M.: Deep meta learning for real-time target-aware visual tracking. In: ICCV (2019)

    Google Scholar 

  5. Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M., et al.: Eco: efficient convolution operators for tracking. In: CVPR (2017)

    Google Scholar 

  6. Danelljan, M., Robinson, A., Shahbaz Khan, F., Felsberg, M.: Beyond correlation filters: learning continuous convolution operators for visual tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 472–488. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_29

    Chapter  Google Scholar 

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

    Google Scholar 

  8. Dong, X., Shen, J.: Triplet loss in Siamese network for object tracking. In: ECCV (2018)

    Google Scholar 

  9. Dong, X., Shen, J., Shao, L., Van Gool, L.: Sub-Markov random walk for image segmentation. IEEE TIP 25, 516–527 (2015)

    MathSciNet  MATH  Google Scholar 

  10. Dong, X., Shen, J., Wang, W., Liu, Y., Shao, L., Porikli, F.: Hyperparameter optimization for tracking with continuous deep q-learning. In: CVPR (2018)

    Google Scholar 

  11. Dong, X., Shen, J., Wang, W., Shao, L., Ling, H., Porikli, F.: Dynamical hyperparameter optimization via deep reinforcement learning in tracking. IEEE TPAMI (2019)

    Google Scholar 

  12. Dong, X., Shen, J., Wu, D., Guo, K., Jin, X., Porikli, F.: Quadruplet network with one-shot learning for fast visual object tracking. IEEE TIP 28, 3516–3527 (2019)

    MathSciNet  MATH  Google Scholar 

  13. Fan, H., et al.: Lasot: a high-quality benchmark for large-scale single object tracking. In: CVPR (2019)

    Google Scholar 

  14. Fan, H., Ling, H.: Siamese cascaded region proposal networks for real-time visual tracking. In: CVPR (2019)

    Google Scholar 

  15. Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML (2017)

    Google Scholar 

  16. Finn, C., Xu, K., Levine, S.: Probabilistic model-agnostic meta-learning. In: NeurIPS (2018)

    Google Scholar 

  17. Galoogahi, H.K., Fagg, A., Huang, C., Ramanan, D., Lucey, S.: Need for speed: A benchmark for higher frame rate object tracking. In: ICCV (2017)

    Google Scholar 

  18. Guo, Q., Feng, W., Zhou, C., Huang, R., Wan, L., Wang, S.: Learning dynamic siamese network for visual object tracking. In: ICCV (2017)

    Google Scholar 

  19. He, A., Luo, C., Tian, X., Zeng, W.: A twofold siamese network for real-time object tracking. In: CVPR (2018)

    Google Scholar 

  20. 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 

  21. Henriques, J.F., Rui, C., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE TPAMI 37, 583–596 (2015)

    Article  Google Scholar 

  22. Hinton, G.E., Plaut, D.C.: Using fast weights to deblur old memories. In: CCSS (1987)

    Google Scholar 

  23. Hochreiter, S., Younger, A.S., Conwell, P.R.: Learning to learn using gradient descent. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 87–94. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44668-0_13

    Chapter  Google Scholar 

  24. Hong, S., You, T., Kwak, S., Han, B.: Online tracking by learning discriminative saliency map with convolutional neural network. In: ICML (2015)

    Google Scholar 

  25. Huang, C., Lucey, S., Ramanan, D.: Learning policies for adaptive tracking with deep feature cascades. In: ICCV (2017)

    Google Scholar 

  26. Khan, S., Hayat, M., Zamir, S.W., Shen, J., Shao, L.: Striking the right balance with uncertainty. In: CVPR (2019)

    Google Scholar 

  27. Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML deep learning workshop (2015)

    Google Scholar 

  28. Kristan, M., et al.: The seventh visual object tracking vot2019 challenge results (2019)

    Google Scholar 

  29. Kristan, M., et al.: A novel performance evaluation methodology for single-target trackers. IEEE TPAMI 38, 2137–2155 (2016)

    Article  Google Scholar 

  30. Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., Yan, J.: Siamrpn++: evolution of siamese visual tracking with very deep networks. In: CVPR (2019)

    Google Scholar 

  31. Li, B., Yan, J., Wu, W., Zhu, Z., Hu, X.: High performance visual tracking with siamese region proposal network. In: CVPR (2018)

    Google Scholar 

  32. Li, H., Dong, W., Mei, X., Ma, C., Huang, F., Hu, B.G.: Lgm-net: learning to generate matching networks for few-shot learning. In: ICML (2019)

    Google Scholar 

  33. Li, P., Chen, B., Ouyang, W., Wang, D., Yang, X., Lu, H.: Gradnet: gradient-guided network for visual object tracking. In: ICCV (2019)

    Google Scholar 

  34. Li, S., Yeung, D.Y.: Visual object tracking for unmanned aerial vehicles: a benchmark and new motion models. In: AAAI (2017)

    Google Scholar 

  35. 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 

  36. Liu, Y., Dong, X., Lu, X., Khan, F.S., Shen, J., Hoi, S.: Teacher-Students Knowledge Distillation for Siamese Trackers. arXiv (2019)

    Google Scholar 

  37. Lu, X., Ma, C., Ni, B., Yang, X., Reid, I., Yang, M.H.: Deep regression tracking with shrinkage loss. In: ECCV (2018)

    Google Scholar 

  38. Lu, X., Wang, W., Shen, J., Tai, Y.W., Crandall, D.J., Hoi, S.C.: Learning video object segmentation from unlabeled videos. In: CVPR (2020)

    Google Scholar 

  39. Ma, B., Hu, H., Shen, J., Zhang, Y., Porikli, F.: Linearization to nonlinear learning for visual tracking. In: ICCV (2015)

    Google Scholar 

  40. Ma, B., Shen, J., Liu, Y., Hu, H., Shao, L., Li, X.: Visual tracking using strong classifier and structural local sparse descriptors. IEEE TMM 17, 1818–1828 (2015)

    Google Scholar 

  41. 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 

  42. Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: CVPR (2016)

    Google Scholar 

  43. Park, E., Berg, A.C.: Meta-tracker: fast and robust online adaptation for visual object trackers. In: ECCV (2018)

    Google Scholar 

  44. Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: ICLR (2017)

    Google Scholar 

  45. 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: CVPR (2017)

    Google Scholar 

  46. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: NeurIPS (2015)

    Google Scholar 

  47. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. IJCV 115, 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y

    Article  MathSciNet  Google Scholar 

  48. Rusu, A.A., et al.: Meta-learning with latent embedding optimization. In: ICLR (2019)

    Google Scholar 

  49. Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: Meta-learning with memory-augmented neural networks. In: ICML (2016)

    Google Scholar 

  50. Schmidhuber, J.: Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-... hook. Ph.D. thesis, Technische Universität München (1987)

    Google Scholar 

  51. Shen, J., Tang, X., Dong, X., Shao, L.: Visual object tracking by hierarchical attention siamese network. IEEE TCYB 50, 3068–3080 (2020)

    Google Scholar 

  52. Shen, J., Yu, D., Deng, L., Dong, X.: Fast online tracking with detection refinement. IEEE TITS 19, 162–173 (2017)

    Google Scholar 

  53. Shen, Z., Lai, W.S., Xu, T., Kautz, J., Yang, M.H.: Exploiting semantics for face image deblurring. IJCV 128, 1829–1846 (2020). https://doi.org/10.1007/s11263-019-01288-9

    Article  Google Scholar 

  54. Shen, Z., et al.: Human-aware motion deblurring. In: ICCV (2019)

    Google Scholar 

  55. Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: NeurIPS (2017)

    Google Scholar 

  56. Song, Y., et al.: Vital: visual tracking via adversarial learning. In: CVPR (2018)

    Google Scholar 

  57. Thrun, S., Pratt, L.: Learning to learn: introduction and overview. In: Thrun, S., Pratt, L. (eds.) Learning to learn, pp. 3–17. Springer, Boston (1998). https://doi.org/10.1007/978-1-4615-5529-2_1

    Chapter  MATH  Google Scholar 

  58. Valmadre, J., Bertinetto, L., Henriques, J.F., Vedaldi, A., Torr, P.H.: End-to-end representation learning for correlation filter based tracking. In: CVPR (2017)

    Google Scholar 

  59. Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. In: NeurIPS (2016)

    Google Scholar 

  60. 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: CVPR (2018)

    Google Scholar 

  61. Wang, W., Shen, J., Dong, X., Borji, A.: Salient object detection driven by fixation prediction. In: CVPR (2018)

    Google Scholar 

  62. Wang, W., Shen, J., Dong, X., Borji, A., Yang, R.: Inferring salient objects from human fixations. IEEE TPAMI 42, 1913–1927 (2019)

    Article  Google Scholar 

  63. Wang, X., Li, C., Luo, B., Tang, J.: Sint++: robust visual tracking via adversarial positive instance generation. In: CVPR (2018)

    Google Scholar 

  64. Yang, T., Chan, A.B.: Learning dynamic memory networks for object tracking. In: ECCV (2018)

    Google Scholar 

  65. Yi, W., Jongwoo, L., Yang, M.H.: Object tracking benchmark. IEEE TPAMI (2015)

    Google Scholar 

  66. Yin, J., Wang, W., Meng, Q., Yang, R., Shen, J.: A unified object motion and affinity model for online multi-object tracking. In: CVPR (2020)

    Google Scholar 

  67. Zhang, Y., Wang, L., Qi, J., Wang, D., Feng, M., Lu, H.: Structured siamese network for real-time visual tracking. In: ECCV (2018)

    Google Scholar 

  68. Zhang, Z., Peng, H.: Deeper and wider siamese networks for real-time visual tracking. In: CVPR (2019)

    Google Scholar 

  69. Zhu, Z., Wang, Q., Li, B., Wu, W., Yan, J., Hu, W.: Distractor-aware siamese networks for visual object tracking. In: ECCV (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Inception Institute of Artificial Intelligence, Abu Dhabi, UAE

    Xingping Dong, Jianbing Shen & Ling Shao

  2. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

    Ling Shao

  3. Australian National University, Canberra, Australia

    Fatih Porikli

Authors
  1. Xingping Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianbing Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ling Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fatih Porikli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianbing Shen .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dong, X., Shen, J., Shao, L., Porikli, F. (2020). CLNet: A Compact Latent Network for Fast Adjusting Siamese Trackers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12365. Springer, Cham. https://doi.org/10.1007/978-3-030-58565-5_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58565-5_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58564-8

  • Online ISBN: 978-3-030-58565-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy