Detecting Tracking Failures from Correlation Response Maps | SpringerLink
Skip to main content
Springer Nature Link
Log in

Detecting Tracking Failures from Correlation Response Maps

  • Conference paper
  • First Online:
Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

Included in the following conference series:

Abstract

Tracking methods based on correlation filters have gained popularity in recent years due to their robustness to rotations, occlusions, and other challenging aspects of visual tracking. Such methods generate a confidence or response map which is used to estimate the new location of the tracked target. By examining the features of this map, important details about the tracker status can be inferred and compensatory measures can be taken in order to minimize failures. We propose an algorithm that uses the mean and entropy of this response map to prevent bad target model updates caused by problems such as occlusions and motion blur as well as to determine the size of the target search area. Quantitative experiments demonstrate that our method improves success plots over a baseline tracker that does not incorporate our failure detection mechanism.

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
¥17,985 /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
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 11439
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 14299
Price includes VAT (Japan)
  • 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

Notes

  1. 1.

    Because of implementation difficulties, our evaluation excludes the redTeam sequence and covers only 99 of the original 100 sequences.

References

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

  2. Tang, M., Feng, J.: Multi-kernel correlation filter for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3038–3046 (2015)

    Google Scholar 

  3. Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: The IEEE International Conference on Computer Vision (ICCV) (2015)

    Google Scholar 

  4. Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Hierarchical convolutional features for visual tracking. In: The IEEE International Conference on Computer Vision (ICCV) (2015)

    Google Scholar 

  5. Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking (2015). arXiv preprint arXiv:1510.07945

  6. Zhu, G., Wang, J., Lu, H.: Clustering based ensemble correlation tracking. Comput. Vis. Image Underst. (2016)

    Google Scholar 

  7. Lukežič, A., Čehovin, L., Kristan, M.: Deformable parts correlation filters for robust visual tracking (2016). arXiv preprint arXiv:1605.03720

  8. Akin, O., Erdem, E., Erdem, A., Mikolajczyk, K.: Deformable part-based tracking by coupled global and local correlation filters. J. Vis. Commun. Image Represent. 38, 763–774 (2016)

    Article  Google Scholar 

  9. Kalal, Z., Mikolajczyk, K., Matas, J.: Forward-backward error: automatic detection of tracking failures. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 2756–2759. IEEE (2010)

    Google Scholar 

  10. Biresaw, T.A., Alvarez, M.S., Regazzoni, C.S.: Online failure detection and correction for bayesian sparse feature-based object tracking. In: 2011 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp. 320–324. IEEE (2011)

    Google Scholar 

  11. Cordes, K., Müller, O., Rosenhahn, B., Ostermann, J.: Feature trajectory retrieval with application to accurate structure and motion recovery. In: Bebis, G., et al. (eds.) ISVC 2011. LNCS, vol. 6938, pp. 156–167. Springer, Heidelberg (2011). doi:10.1007/978-3-642-24028-7_15

    Chapter  Google Scholar 

  12. Zhang, J., Ma, S., Sclaroff, S.: MEEM: robust tracking via multiple experts using entropy minimization. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 188–203. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10599-4_13

    Google Scholar 

  13. 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)

    Google Scholar 

  14. Wu, Y., Hu, J., Li, F., Cheng, E., Yu, J., Ling, H.: Kernel-based motion-blurred target tracking. In: Bebis, G., et al. (eds.) ISVC 2011. LNCS, vol. 6939, pp. 486–495. Springer, Heidelberg (2011). doi:10.1007/978-3-642-24031-7_49

    Chapter  Google Scholar 

  15. Siena, S., Kumar, B.V.: Detecting occlusion from color information to improve visual tracking. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1110–1114. IEEE (2016)

    Google Scholar 

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

    Article  Google Scholar 

  17. Kristan, M., Matas, J., Leonardis, A., Felsberg, M., Cehovin, L., Fernandez, G., Vojir, T., Hager, G., Nebehay, G., Pflugfelder, R.: The visual object tracking VOT2015 challenge results. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 1–23 (2015)

    Google Scholar 

  18. 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)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Marquette University, 1551 W. Wisconsin Ave, Milwaukee, Wisconsin, 53233, USA

    Ryan Walsh & Henry Medeiros

Authors
  1. Ryan Walsh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Henry Medeiros
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Henry Medeiros .

Editor information

Editors and Affiliations

  1. University of Nevada , Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center , Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory , Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute , Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Walsh, R., Medeiros, H. (2016). Detecting Tracking Failures from Correlation Response Maps. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50835-1_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation