Skip to main content
Springer Nature Link
Log in

Learning Spatial Fusion and Matching for Visual Object Tracking

  • Conference paper
  • First Online:
PRICAI 2022: Trends in Artificial Intelligence (PRICAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13631))

Included in the following conference series:

  • 1455 Accesses

Abstract

Siamese network based trackers have achieved outstanding performance in visual object tracking, which in essence is the application of the efficient cross-correlation as the matching function. However, it is experimentally found that the cross-correlation based matching function is difficult to generate accurate tracking results in some challenging environments, such as background clutters and fast motion. Thus, a new Siamese-based tracker named SiamFAM is proposed. Specifically, from the perspective of feature fusion, a new matching function named Concatenation is introduced into our tracker, which can reduce the influence of background clutters by fine-grained matching with little computational overhead. Meanwhile, an adaptively spatial feature fusion (ASFF) module is proposed, which can take full use of multi-layer features and reduce poor prediction results during the prediction process. In addition, a refinement module is adopted to reduce the occurrence of tracking drift. Extensive experiments are conducted on six challenging benchmarks, including VOT2016, VOT2019, UAV123, NFS, OTB100, and LaSOT, demonstrating that our tracker is practical and can achieve a leading performance.

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

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. Bhat, G., Danelljan, M., Gool, L.V., Timofte, R.: Learning discriminative model prediction for tracking. In: ICCV, pp. 6182–6191 (2019)

    Google Scholar 

  3. Bhat, G., Johnander, J., Danelljan, M., Khan, F.S., Felsberg, M.: Unveiling the power of deep tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 493–509. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01216-8_30

    Chapter  Google Scholar 

  4. Chen, Z., Zhong, B., Li, G., Zhang, S., Ji, R.: Siamese box adaptive network for visual tracking. In: CVPR, pp. 6668–6677 (2020)

    Google Scholar 

  5. Cheng, S., et al.: Learning to filter: Siamese relation network for robust tracking. In: CVPR, pp. 4421–4431 (2021)

    Google Scholar 

  6. Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: Atom: accurate tracking by overlap maximization. In: CVPR, pp. 4660–4669 (2019)

    Google Scholar 

  7. Danelljan, M., Bhat, G., Shahbaz Khan, F., Felsberg, M.: Eco: efficient convolution operators for tracking. In: CVPR, pp. 6638–6646 (2017)

    Google Scholar 

  8. Fan, H., et al.: LaSOT: a high-quality benchmark for large-scale single object tracking. In: CVPR, pp. 5374–5383 (2019)

    Google Scholar 

  9. Guo, D., Shao, Y., Cui, Y., Wang, Z., Zhang, L., Shen, C.: Graph attention tracking. In: CVPR, pp. 9543–9552 (2021)

    Google Scholar 

  10. Guo, D., Wang, J., Cui, Y., Wang, Z., Chen, S.: SiamCAR: Siamese fully convolutional classification and regression for visual tracking. In: CVPR, pp. 6269–6277 (2020)

    Google Scholar 

  11. Han, W., Dong, X., Khan, F.S., Shao, L., Shen, J.: Learning to fuse asymmetric feature maps in Siamese trackers. In: CVPR, pp. 16570–16580 (2021)

    Google Scholar 

  12. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)

    Google Scholar 

  13. 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 

  14. Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)

  15. 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 (2019)

    Article  Google Scholar 

  16. Kiani Galoogahi, H., Fagg, A., Huang, C., Ramanan, D., Lucey, S.: Need for speed: a benchmark for higher frame rate object tracking. In: ICCV, pp. 1125–1134 (2017)

    Google Scholar 

  17. Kristan, M., et al.: The visual object tracking VOT2014 challenge results. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 191–217. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16181-5_14

    Chapter  Google Scholar 

  18. Kristan, M., et al.: The seventh visual object tracking vot2019 challenge results. In: ICCV Workshops, pp. 2206–2241 (2019)

    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: 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: 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. Liu, S., Huang, D., Wang, Y.: Learning spatial fusion for single-shot object detection. arXiv preprint arXiv:1911.09516 (2019)

  23. 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 

  24. Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: CVPR, pp. 4293–4302 (2016)

    Google Scholar 

  25. Peng, J., et al.: SiamRCR: reciprocal classification and regression for visual object tracking. arXiv preprint arXiv:2105.11237 (2021)

  26. 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, pp. 5296–5305 (2017)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  28. Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: CVPR, pp. 1199–1208 (2018)

    Google Scholar 

  29. Tao, R., Gavves, E., Smeulders, A.W.: Siamese instance search for tracking. In: CVPR, pp. 1420–1429 (2016)

    Google Scholar 

  30. Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H.: End-to-end representation learning for correlation filter based tracking. In: CVPR, pp. 2805–2813 (2017)

    Google Scholar 

  31. Wang, G., Luo, C., Xiong, Z., Zeng, W.: SPM-tracker: series-parallel matching for real-time visual object tracking. In: CVPR, pp. 3643–3652 (2019)

    Google Scholar 

  32. Wang, Q., Zhang, L., Bertinetto, L., Hu, W., Torr, P.H.: Fast online object tracking and segmentation: a unifying approach. In: CVPR, pp. 1328–1338 (2019)

    Google Scholar 

  33. Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: CVPR, pp. 2411–2418 (2013)

    Google Scholar 

  34. Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: CVPR, pp. 1492–1500 (2017)

    Google Scholar 

  35. 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, pp. 12549–12556 (2020)

    Google Scholar 

  36. Yan, B., Zhang, X., Wang, D., Lu, H., Yang, X.: Alpha-refine: boosting tracking performance by precise bounding box estimation. In: CVPR, pp. 5289–5298 (2021)

    Google Scholar 

  37. Yu, Y., Xiong, Y., Huang, W., Scott, M.R.: Deformable Siamese attention networks for visual object tracking. In: CVPR, pp. 6728–6737 (2020)

    Google Scholar 

  38. Zhang, L., Gonzalez-Garcia, A., van de Weijer, J., Danelljan, M., Khan, F.S.: Learning the model update for Siamese trackers. In: ICCV, pp. 4010–4019 (2019)

    Google Scholar 

  39. Zhang, Z., Liu, Y., Wang, X., Li, B., Hu, W.: Learn to match: automatic matching network design for visual tracking. In: ICCV, pp. 13339–13348 (2021)

    Google Scholar 

  40. Zhang, Z., Peng, H.: Deeper and wider Siamese networks for real-time visual tracking. In: CVPR, pp. 4591–4600 (2019)

    Google Scholar 

  41. Zhang, Z., Peng, H., Fu, J., Li, B., Hu, W.: Ocean: object-aware anchor-free tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12366, pp. 771–787. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58589-1_46

    Chapter  Google Scholar 

  42. Zhu, Z., Wang, Q., Li, B., Wu, W., Yan, J., Hu, W.: Distractor-aware Siamese networks for visual object tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 103–119. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_7

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Wei Xiao & Zili Zhang

Authors
  1. Wei Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zili Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zili Zhang .

Editor information

Editors and Affiliations

  1. CSIRO Australian e-Health Research Centre, Brisbane, QLD, Australia

    Sankalp Khanna

  2. Shanghai Jiao Tong University, Shanghai, China

    Jian Cao

  3. University of Tasmania, Hobart, TAS, Australia

    Quan Bai

  4. University of Technology Sydney, Sydney, NSW, Australia

    Guandong Xu

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xiao, W., Zhang, Z. (2022). Learning Spatial Fusion and Matching for Visual Object Tracking. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13631. Springer, Cham. https://doi.org/10.1007/978-3-031-20868-3_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20868-3_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20867-6

  • Online ISBN: 978-3-031-20868-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics