Skip to main content
SpringerLink
Log in

UTB180: A High-Quality Benchmark for Underwater Tracking

  • Conference paper
  • First Online:
Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13845))

Included in the following conference series:

Abstract

Deep learning methods have demonstrated encouraging performance on open-air visual object tracking (VOT) benchmarks, however, their strength remains unexplored on underwater video sequences due to the lack of challenging underwater VOT benchmarks. Apart from the open-air tracking challenges, videos captured in underwater environments pose additional challenges for tracking such as low visibility, poor video quality, distortions in sharpness and contrast, reflections from suspended particles, and non-uniform lighting. In the current work, we propose a new Underwater Tracking Benchmark (UTB180) dataset consisting of 180 sequences to facilitate the development of underwater deep trackers. The sequences in UTB180 are selected from both underwater natural and online sources with over 58,000 annotated frames. Video-level attributes are also provided to facilitate the development of robust trackers for specific challenges. We benchmark 15 existing pre-trained State-Of-The-Art (SOTA) trackers on UTB180 and compare their performance on another publicly available underwater benchmark. The trackers consistently perform worse on UTB180 showing that it poses more challenging scenarios. Moreover, we show that fine-tuning five high-quality SOTA trackers on UTB180 still does not sufficiently boost their tracking performance. Our experiments show that the UTB180 sequences pose a major burden on the SOTA trackers as compared to their open-air tracking performance. The performance gap reveals the need for a dedicated end-to-end underwater deep tracker that takes into account the inherent properties of underwater environments. We believe that our proposed dataset will be of great value to the tracking community in advancing the SOTA in underwater VOT. Our dataset is publicly available on Kaggle.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
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

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., Van Gool, L., Timofte, R.: Learning discriminative model prediction for tracking. Proc. In: IEEE Int. Conf. Comput. Vis. 2019-Oct (ICCV), pp. 6181–6190 (2019). https://doi.org/10.1109/ICCV.2019.00628

  3. Bhat, G., Danelljan, M., Van Gool, L., Timofte, R.: Know Your Surroundings: Exploiting Scene Information for Object Tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12368, pp. 205–221. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58592-1_13

    Chapter  Google Scholar 

  4. Boudiaf, A., et al.: Underwater image enhancement using pre-trained transformer. In: International Conference on Image Analysis and Processing, pp. 480–488. Springer (2022). https://doi.org/10.1007/978-3-031-06433-3_41

  5. Chen, X., Yan, B., Zhu, J., Wang, D., Yang, X., Lu, H.: Transformer Tracking, pp. 8122–8131 (2021). https://doi.org/10.1109/cvpr46437.2021.00803

  6. Chen, Z., Zhong, B., Li, G., Zhang, S., Ji, R.: Siamese Box Adaptive Network for Visual Tracking. Proc. In: IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp. 6667–6676 (2020). https://doi.org/10.1109/CVPR42600.2020.00670

  7. CVAT: Computer Vision Annotation Tool. https://cvat.org

  8. Danelljan, M., Van Gool, L., Timofte, R.: Probabilistic regression for visual tracking. Proc. In: IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp. 7181–7190 (2020). https://doi.org/10.1109/CVPR42600.2020.00721

  9. Fan, H., et al.: LaSOT: a High-quality Large-scale Single Object Tracking Benchmark. Int. J. Comput. Vis. 129(2), 439–461 (2021). https://doi.org/10.1007/s11263-020-01387-y

  10. Giraldo, J.H., Javed, S., Bouwmans, T.: Graph moving object segmentation. In: IEEE Trans. Pattern Anal. Mach. Intell. 44(5), 2485-2503 (2020)

    Google Scholar 

  11. Guo, D., Shao, Y., Cui, Y., Wang, Z., Zhang, L., Shen, C.: Graph Attention Tracking. Proc. In: IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp. 9538–9547 (2021). https://doi.org/10.1109/CVPR46437.2021.00942

  12. Guo, D., Wang, J., Cui, Y., Wang, Z., Chen, S.: SiamCAR: siamese Fully Convolutional Classification and Regression for Visual Tracking. Proc. In: IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp. 6268–6276 (2020). https://doi.org/10.1109/CVPR42600.2020.00630

  13. 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 

  14. Han, M., Lyu, Z., Qiu, T., Xu, M.: A Review on Intelligence Dehazing and Color Restoration for Underwater Images. In: IEEE Trans. Syst. Man, Cybern. Syst. 50(5), 1820–1832 (2020). https://doi.org/10.1109/TSMC.2017.2788902

  15. Huang, L., Zhao, X., Huang, K.: Got-10k: a large high-diversity benchmark for generic object tracking in the wild. In: IEEE Trans. Pattern Anal. Mach. Intell. 43(5), 1562–1577 (2021). https://doi.org/10.1109/TPAMI.2019.2957464. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103976016 &doi=10.1109%2FTPAMI.2019.2957464 &partnerID=40 &md5=3fd7d1e870e60df363a83a52a092c544

  16. Javed, S., Danelljan, M., Khan, F.S., Khan, M.H., Felsberg, M., Matas, J.: Visual Object Tracking with Discriminative Filters and Siamese Networks: a Survey and Outlook 14(8), 1–20 (2021). http://arxiv.org/abs/2112.02838

  17. Javed, S., Dias, J., Werghi, N.: Low-rank tensor tracking. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 605–614 (2019). https://doi.org/10.1109/ICCVW.2019.00074

  18. Javed, S., Mahmood, A., Dias, J., Seneviratne, L., Werghi, N.: Hierarchical spatiotemporal graph regularized discriminative correlation filter for visual object tracking. In: IEEE Transactions on Cybernetics (2021)

    Google Scholar 

  19. Javed, S., Mahmood, A., Dias, J., Werghi, N.: Robust structural low-rank tracking. IEEE Trans. Image Process. 29, 4390–4405 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  20. Javed, S., et al.: A novel algorithm based on a common subspace fusion for visual object tracking. IEEE Access 10, 24690–24703 (2022)

    Article  Google Scholar 

  21. Javed, S., Zhang, X., Dias, J., Seneviratne, L., Werghi, N.: Spatial Graph Regularized Correlation Filters for Visual Object Tracking. In: Abraham, A., Ohsawa, Y., Gandhi, N., Jabbar, M.A., Haqiq, A., McLoone, S., Issac, B. (eds.) SoCPaR 2020. AISC, vol. 1383, pp. 186–195. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73689-7_19

    Chapter  Google Scholar 

  22. Javed, S., Zhang, X., Dias, J., Seneviratne, L., Werghi, N.: Spatial Graph Regularized Correlation Filters for Visual Object Tracking. In: Abraham, A., Abraham, A., et al. (eds.) SoCPaR 2020. AISC, vol. 1383, pp. 186–195. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73689-7_19

    Chapter  Google Scholar 

  23. Kristan, M., Leonardis, A., Matas, e.a.: The Visual Object Tracking VOT2016 Challenge Results, pp. 777–823. Springer International Publishing (2016). https://doi.org/10.1007/978-3-319-48881-3_54

  24. Kristan, M., Leonardis, A., Matas, e.: The Sixth Visual Object Tracking VOT2018 Challenge Results. In: Leal-Taixé, L., Roth, S. (eds.) Comput. Vis. - ECCV 2018 Work, pp. 3–53. Springer International Publishing, Cham (2019) https://doi.org/10.1007/978-3-030-11009-3_1

  25. Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., Yan, J.: SIAMRPN++: evolution of siamese visual tracking with very deep networks. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2019-June, 4277–4286 (2019). https://doi.org/10.1109/CVPR.2019.00441

  26. Li, B., Yan, J., Wu, W., Zhu, Z., Hu, X.: High Performance Visual Tracking with Siamese Region Proposal Network. In: IEEE Conf. Comput. Vis. Pattern Recognit, pp. 8971–8980 (2018)

    Google Scholar 

  27. Liang, P., Blasch, E., Ling, H.: Encoding color information for visual tracking: Algorithms and benchmark. IEEE Trans. Image Process. 24(12), 5630–5644 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  28. Mayer, C., Danelljan, M., Pani Paudel, D., Van Gool, L.: Learning Target Candidate Association to Keep Track of What Not to Track (ICCV), 13424–13434 (2022). https://doi.org/10.1109/iccv48922.2021.01319

  29. Meinhardt, T., Kirillov, A., Leal-Taixé, L., Feichtenhofer, C.: Trackformer: multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8844–8854 (2022)

    Google Scholar 

  30. 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 

  31. Müller, M., Bibi, A., Giancola, S., Alsubaihi, S., Ghanem, B.: TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 310–327. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01246-5_19

    Chapter  Google Scholar 

  32. Panetta, K., Kezebou, L., Oludare, V., Agaian, S.: Comprehensive Underwater Object Tracking Benchmark Dataset and Underwater Image Enhancement with GAN. IEEE J. Ocean. Eng. 47(1), 59–75 (2022). https://doi.org/10.1109/JOE.2021.3086907

    Article  Google Scholar 

  33. Pexel: 1,363+ Best Free Underwater 4K Stock Video Footage & Royalty-Free HD Video Clips. https://www.pexels.com/search/videos/underwater/

  34. Underwaterchangedetection: Videos - Underwaterchangedetection. http://underwaterchangedetection.eu/Videos.html

  35. Wang, N., Zhou, W., Wang, J., Li, H.: Transformer Meets Tracker: exploiting Temporal Context for Robust Visual Tracking. Proc. In: IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp. 1571–1580 (2021). https://doi.org/10.1109/CVPR46437.2021.00162

  36. Wang, Q., Zhang, L., Bertinetto, L., Hu, W., Torr, P.H.: Fast online object tracking and segmentation: a unifying approach. Proc. In: IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2019-June, 1328–1338 (2019). https://doi.org/10.1109/CVPR.2019.00142

  37. Wu, Y., Lim, J., Yang, M.H.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834–1848 (2015). https://doi.org/10.1109/TPAMI.2014.2388226

    Article  Google Scholar 

  38. Xu, Y., Wang, Z., Li, Z., Yuan, Y., Yu, G.: SiamFC++: Towards robust and accurate visual tracking with target estimation guidelines. AAAI 2020–34th AAAI Conf. Artif. Intell. 34(7), 12549–12556 (2020). https://doi.org/10.1609/aaai.v34i07.6944

  39. Yan, B., Peng, H., Fu, J., Wang, D., Lu, H.: Learning Spatio-Temporal Transformer for Visual Tracking, pp. 10428–10437 (2022). https://doi.org/10.1109/iccv48922.2021.01028

  40. Yu, Y., Xiong, Y., Huang, W., Scott, M.R.: Deformable Siamese Attention Networks for Visual Object Tracking. Proc. In: IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp. 6727–6736 (2020). https://doi.org/10.1109/CVPR42600.2020.00676

  41. Zhao, M., Okada, K., Inaba, M.: TrTr: visual Tracking with Transformer (2021). http://arxiv.org/abs/2105.03817

Download references

Acknowledgments

This publication acknowledges the support provided by the Khalifa University of Science and Technology under Faculty Start Up grants FSU-2022–003 Award No. 84740 0 0401.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Abu Dhabi, UAE

    Basit Alawode, Yuhang Guo, Mehnaz Ummar, Naoufel Werghi, Jorge Dias & Sajid Javed

  2. The University of Western Australia, Stirling Highway, Perth, Australia

    Ajmal Mian

Authors
  1. Basit Alawode
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuhang Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mehnaz Ummar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Naoufel Werghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jorge Dias
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ajmal Mian
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sajid Javed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sajid Javed .

Editor information

Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 911 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2023 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

Alawode, B. et al. (2023). UTB180: A High-Quality Benchmark for Underwater Tracking. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13845. Springer, Cham. https://doi.org/10.1007/978-3-031-26348-4_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-26348-4_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26347-7

  • Online ISBN: 978-3-031-26348-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation