Skip to main content
SpringerLink
Log in

Online Multi-object Tracking Based on Deep Learning

  • Conference paper
  • First Online:
Communications, Signal Processing, and Systems (CSPS 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 878))

  • 2319 Accesses

Abstract

Multi-object tracking task aims to identify and track all targets in the video. It has important applications in intelligent monitoring and other fields. Two problems can affect the accuracy of the multi-object tracking task. First, occlusion between targets will lead to interruption of tracking trajectory and switch of tracking target. Second, quality of the object detection results will directly affect the tracking accuracy. In this paper, we adopt a single-object tracking algorithm based on deep learning is introduced to solve the first problem and develop a discriminant network scoring the accuracy of detection and prediction bounding boxes to solve the second problem. The experimental results show that the proposed tracker performs better than other competing methods.

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 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover 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. Bose, B., Wang, X., Grimson, E.: Multi-class object tracking algorithm that handles fragmentation and grouping. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

  2. Song, B., Jeng, T.Y., Staudt, E., Roy-Chowdhury, A.K.: A stochastic graph evolution framework for robust multi-target tracking, European Conference on Computer Vision, pp. 605–619 (2010)

    Google Scholar 

  3. Hu, W., Li, X., Luo, W., Zhang, X., Maybank, S., Zhang, Z.: Single and multiple object tracking using log-euclidean riemannian subspace and block-division appearance model. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2420–2440 (2012)

    Article  Google Scholar 

  4. Zhang, L., van der Maaten, L.: Structure preserving object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1838–1845 (2013)

    Google Scholar 

  5. Yang, M., Yu, T., Wu, Y.: Game-theoretic multiple target tracking. In: IEEE International Conference on Computer Vision, pp. 1–8 (2007)

    Google Scholar 

  6. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

    Google Scholar 

  7. Girshick, R.: Fast R-CNN. In: IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

    Google Scholar 

  8. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)

    Google Scholar 

  9. Brostow, G., Cipolla, R.: Unsupervised bayesian detection of independent motion in crowds. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 594–601 (2006)

    Google Scholar 

  10. Okuma, K., Taleghani, A., De Freitas, N., Little, J.J., Lowe, D.G.: A boosted particle filter: Multitarget detection and tracking. In: European Conference on Computer Vision, pp. 28–39 (2004)

    Google Scholar 

  11. Ali, S., Shah, M.: Floor fields for tracking in high density crowd scenes. In: European Conference on Computer Vision, pp. 1–14 (2008)

    Google Scholar 

  12. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)

    Google Scholar 

  13. Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: IEEE International Conference on Image Processing, pp. 3645–3649 (2017)

    Google Scholar 

  14. Milan, A., Leal-Taixé, L., Reid, I., Roth, S., Schindler, K.: MOT16: a benchmark for multi-object tracking, arXiv preprint, arXiv:1603.00831 (2016)

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

    Google Scholar 

  16. Sanchez-Matilla, R., Poiesi, F., Cavallaro, A.: Multi-target tracking with strong and weak detections. In: European Conference on Computer Vision Workshops, pp. 84–99 (2016)

    Google Scholar 

  17. Geiger, A., Lauer, M., Wojek, C., Stiller, C., Urtasun, R.: 3D traffic scene understanding from movable platforms. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 1012–1025 (2013)

    Article  Google Scholar 

  18. Rezatofighi, H., Milan, A., Zhang, Z., Shi, Q., Dick, A., Reid, I.: Joint probabilistic data association revisited. In: IEEE International Conference on Computer Vision, pp. 3047–3055 (2015)

    Google Scholar 

Download references

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant 61806037, in part by the Natural Science Foundation of Liaoning Province under Grant 2019-MS067, and in part by the Youth Technology Star Project of Dalian City under Grant 2018RQ57.

Author information

Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Zheming Sun & Dong Wang

  2. School of Information and Communication Engineering, Dalian Minzu University, Dalian, China

    Chunjuan Bo

Authors
  1. Zheming Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chunjuan Bo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chunjuan Bo .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. Tianjin Normal University, Tianjin, China

    Wei Wang

  3. Dalian University of Technology, Dalian, China

    Xin Liu

  4. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sun, Z., Bo, C., Wang, D. (2022). Online Multi-object Tracking Based on Deep Learning. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2021. Lecture Notes in Electrical Engineering, vol 878. Springer, Singapore. https://doi.org/10.1007/978-981-19-0390-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-0390-8_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-0389-2

  • Online ISBN: 978-981-19-0390-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation