Skip to main content
SpringerLink
Log in

High-speed moving target tracking of multi-camera system with overlapped field of view

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Multi-camera target tracking is a research hot spot in the field of computer vision. Because the field of view covered by the fixed monocular camera is limited, it cannot continuously track fast-moving targets for a long time, such as the video sequence of skaters’ short-track speed skating. However, for such video sequences, the relative displacement of target between frames is relatively large and the advanced tracking algorithm is difficult to achieve accurate tracking, which has a considerable impact on the subsequent target handover. In view of this situation, we use four fixed cameras to build a tracking system to continuously track skater and propose to use convolutional neural network to extract the semantic features of the target to improve the accuracy of the target handover process. Aiming at this kind of fast motion video sequence with a fixed field of view, a new tracking algorithm was proposed. We establish the motion model of the target and integrate the speed information of the target into the sample generation mechanism. In addition, we also construct a scale filter in context to constantly and iteratively update the scale change information of the prediction box to adapt to the significant change of the visual properties of the target. It improves the stability and robustness of the system. Extensive experimental results in object tracking benchmark and our dataset of skaters illustrate outstanding performance of our method compared with the state-of-the-art methods, especially against the fast motion sequences with a fixed field of view.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Jodoin, J.P., Bilodeau, G.A., Saunier, N.: Urban tracker: multiple object tracking in urban mixed traffic. In IEEE winter conference on applications of computer vision, Steamboat Springs, CO, USA, March 24–26 (2014)

  2. Zhang, Y., Zeng, C., Liang, H., et al.: A visual target tracking algorithm based on improved Kernelized Correlation Filters. In IEEE international conference on mechatronics and automation (2016)

  3. Avidan, S.: Support Vector Tracking. PAMI 26(8), 1064–1072 (2004)

    Article  Google Scholar 

  4. Zheng, W.-S., Gong, S., Xiang, T.: Reidentification by relative distance comparison. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 653–668 (2013)

    Article  Google Scholar 

  5. Wang, X., Tang, Z.M.: Modified particle filter-based infrared pedestrian tracking. Infrared Phys. Technol. 53(2), 280–287 (2010)

    Article  Google Scholar 

  6. Cui, X., Wu, Q., Zhou, J.: Online fragments-based scale invariant electro-optic tracking with SIFT. Int. J. Light Electr. Opt. 126(18), 1720–1725 (2015)

    Article  Google Scholar 

  7. Khan, S., Shah, M.: Consistent labeling of tracked objects in multipe cameras with overlapping fields of view. IEEE Trans. Pattern Anal. Mach. Intell. 25(10), 1355–1360 (2003)

    Article  Google Scholar 

  8. Gaxiola, L.N., Diazramirez, V.H., Tapia, J.J.: Performance evaluation of correlation filters for target tracking. J. Bacteriol. 40(11), 376–379 (2015)

    Google Scholar 

  9. Bolme, D.S., Beveridge, J. R., Draper, B. A.: Visual object tracking using adaptive correlation filters. In The twenty-third ieee conference on computer vision and pattern recognition. IEEE (2010)

  10. Ali, A., Jalil, A., Ahmed, J.: Correlation, Kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking. SIViP 9(7), 1567–1585 (2015)

    Article  Google Scholar 

  11. Bertinetto, L., Valmadre, J., Henriques, J.F.: Fully-convolutional Siamese networks for object tracking. In: IEEE International Conference on Computer Vision, pp. 3119–3127 (2015)

  12. Tao, R., Gavves E., Smeulders, A.W.: Siamese instance search for tracking. In IEEE Conference on Computer Vision and Pattern Recognition (2016)

  13. Nam, H., Han, B.: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking. In CVPR (2016)

  14. Zhu, Z., Wang, Q., Li, B., Wu, W., Yan, J., Hu W.: Distractor-aware siamese networks for visual object tracking. In European conference on computer vision (2018)

  15. Li, B., Yan, J., Wu, W., Zhu, Z., Hu, X.: High performance visual tracking with siamese region proposal network. In IEEE conference on computer vision and pattern recognition (2018)

  16. Luo, He, C., Tian, X., Zeng, W.: Towards a better match in siamese network based visual object tracker. In European conference on computer vision workshops (2018)

  17. Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi A., Torr P. H.: Fully-convolutional siamese networks for object tracking. In European conference on computer vision workshops (2016)

  18. Sereda, P., Vilanova, A., Serlie, I.W.O.: Visualization of boundaries in volumetric data sets using LH histograms. IEEE Trans. Visual Comput. Graphics 12(2), 208–218 (2006)

    Article  Google Scholar 

  19. Stark, M., Schiele, B.: How good are local features for classes of geometric objects. In International conference on computer vision. IEEE (2008)

  20. Redmon, J., Farhadi, A.:YOLOv3: An incremental improvement (2018)

  21. Roettger, S., Bauer, M., Stamminger, M.: Spatialized transfer functions. In: Proceedings of the Seventh Joint Eurograph. IEEE VGTC Symposium on Visualization. pp. 271–278 (2005)

  22. Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: Proceedings of IEEE conference on computer vision and pattern recognition (2010)

  23. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels.In Proceedings of the European conference on computer vision (2012)

  24. Wu, Y., Lim, J., and Yang, M.-H.: Online object tracking: A benchmark. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2013)

  25. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. In IEEE transactions on pattern analysis and machine intelligence (2015)

  26. Wang, N., Li, S., Gupta, A., Yeung, D.Y.: Transferring rich feature hierarchies for robust visual tracking (2015)

  27. Han, B., Sim, J., Adam, H.: Branch out: regularization for online ensemble tracking with convolutional neural networks. In: Conference on computer vision and pattern recognition pp. 521–530 (2017)

  28. Breiman, L.: Bagging predictors. Machine Learning. 24(2), 123–140 (1996)

    MATH  Google Scholar 

  29. Ondřej, C., Jiří, M., Kittler J.: Locally optimized RANSAC. Lecture notes in computer science (2003)

  30. Wu, Y., Lim, J., Yang, M.H.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1–1 (2015)

    Article  Google Scholar 

  31. J. F., Henriques, R., Caseiro, P., Martins, Batista, J.: High speed tracking with kernelized correlation filters. In IEEE transactions on pattern analysis and machine intelligence (2015)

  32. Danelljan, M., Häger, G., Khan, F.S., et al.: Discriminative Scale Space Tracking. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1561–1575 (2016)

    Article  Google Scholar 

  33. Danelljan, M., Bhat, G., Khan, F.S., and Felsberg, M.: Eco: Efficient convolution operators for tracking. In IEEE conference on computer vision and pattern recognition (2017)

  34. Tony, L.: Scale invariant feature transform. Scholarpedia. (2012)

  35. Lucena, M. J., Fuertes, J. M., Gomez, J. I., et al.: Optical flow-based probabilistic tracking. (2003)

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 11774031, No. 61705010, No. 61935001), Beijing Science and Technology Project (No. Z181100005918002) and the Winter Olympics Key Project Technology Fund (2018YFF0300804).

Author information

Authors and Affiliations

  1. School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China

    Mi Yan, Yuejin Zhao, Ming Liu, Lingqin Kong & Liquan Dong

Authors
  1. Mi Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuejin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ming Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lingqin Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Liquan Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yuejin Zhao or Ming Liu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, M., Zhao, Y., Liu, M. et al. High-speed moving target tracking of multi-camera system with overlapped field of view. SIViP 15, 1369–1377 (2021). https://doi.org/10.1007/s11760-021-01867-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-021-01867-9

Keywords

Search

Navigation