Multiple object tracking by reliable tracklets | Signal, Image and Video Processing Skip to main content
Springer Nature Link
Log in

Multiple object tracking by reliable tracklets

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

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

This paper proposes a novel network flow model for multi-target tracking, which uses short and highly reliable detection responses as the basic unit, namely the tracklet, in the model. Our model exploits the local information of the tracklet and deploys the global strategy of data association in tracking. The method is divided into two phases: a local phase and a global phase. In the local phase, our method is used to track targets using the detection results, namely the tracking by detection, where the boosted particle filter is used to generate high-confidence detection responses and they are connected into reliable tracklets. In the global phase, the multi-object tracking is modeled as data association problem, and the problem is represented by the maximum posterior probability. Finally, the model is solved by the minimum cost flow algorithm. When dealing with the target occlusion, this paper designs a two-step optimization algorithm to solve the long-term occlusion that affects tracking. A large number of experimental results show that our method is more effective in multi-object tracking than other state-of-the-art methods.

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

Access this article

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

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. http://www.gris.informatik.tu-darmstadt.de/~aandriye/data.html.

  2. http://www.cvg.reading.ac.uk/PETS2009/a.html.

References

  1. Liu, Q., Lu, X., Zhang, C., Chen, W.-S.: Deep convolutional neural networks for thermal infrared object tracking. Knowl. Based Syst. 134, 189–198 (2017)

    Article  Google Scholar 

  2. Li, X., Liu, Q., Wang, H., Zhang, C., Chen, W.-S.: A multi-view model for visual tracking via correlation filters. Knowl. Based Syst. 113, 88–99 (2016)

    Article  Google Scholar 

  3. Ma, X., Liu, Q., Zhang, X., Chen, W.-S.: Visual tracking via exemplar regression model. Knowl. Based Syst. 106, 26–37 (2016)

    Article  Google Scholar 

  4. He, Z., Yi, S., Cheung, Y.-M., You, X., Tang, Y.Y.: Robust object tracking via key patch sparse representation. IEEE Trans. Cybern. 47(2), 354–364 (2016)

    Google Scholar 

  5. Li, X., You, X., Chen, C.L.P.: A novel joint tracker based on occlusion detection. Knowl. Based Syst. 71, 409–418 (2014)

    Article  Google Scholar 

  6. Topkaya, I., Erdogan, H., Porikli, F.: Tracklet clustering for robust multiple object tracking using distance dependent Chinese restaurant processes. Signal Image Video Process. 10(5), 795–802 (2016)

    Article  Google Scholar 

  7. Qi, Y., Zhang, S., Qin, L., et al.: Hedging deep features for visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 2, 1–1 (2018)

    Google Scholar 

  8. Zhang, S., Qi, Y., Jiang, F., et al.: Point-to-set distance metric learning on deep representations for visual tracking. IEEE Trans. Intell. Transp. Syst. 19(1), 187–198 (2018)

    Article  Google Scholar 

  9. Zhang, S., Lan, X., Yao, H., et al.: A biologically inspired appearance model for robust visual tracking. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2357–2370 (2017)

    Article  MathSciNet  Google Scholar 

  10. Zhang, S., Lan, X., Qi, Y., et al.: Robust visual tracking via basis matching. IEEE Trans. Circuits Syst. Video Technol. 27(3), 421–430 (2017)

    Article  Google Scholar 

  11. Zhang, S., Zhou, H., Jiang, F., et al.: Robust visual tracking using structurally random projection and weighted least squares. IEEE Trans. Circuits Syst. Video Technol. 25(11), 1749–1760 (2015)

    Article  Google Scholar 

  12. He, Z., Li, X., You, X., Tao, D., Tang, Y.Y.: Connected component model for multi-object tracking. IEEE Trans. Image Process. 25(8), 3698–3711 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  13. Yi, S., You, X., Cheung, Y.: Single object tracking via robust combination of particle filter and sparse representation. Signal Process. 110, 178–187 (2015)

    Article  Google Scholar 

  14. Cavallaro, A.: Special issue on multi-sensor object detection and tracking. Signal Image Video Process. 1(2), 99–100 (2007)

    Article  Google Scholar 

  15. Lee, B., Erdenee, E., Jin, S., et al.: Efficient object detection using convolutional neural network-based hierarchical feature modeling. Signal Image Video Process. 10(8), 1503–1510 (2016)

    Article  Google Scholar 

  16. Supreeth, H.S.G., Patil, C.M.: Efficient multiple moving object detection and tracking using combined background subtraction and clustering. Signal Image Video Process. 12(6), 1097–1105 (2018)

    Article  Google Scholar 

  17. Sun, X., Yao, H., Lu, X.: Dynamic multi-cue tracking using particle filter. Signal Image Video Process. 8(1), 95–101 (2014)

    Article  Google Scholar 

  18. He, Z., You, X., Tang, Y.Y.: Writer identification of Chinese handwriting documents using hidden Markov tree model. Pattern Recognit 41, 1295–1307 (2008)

    Article  MATH  Google Scholar 

  19. He, Z., Chung, A.C.S.: 3-D B-spline wavelet-based local standard deviation (BWLSD): its application to edge detection and vascular segmentation in magnetic resonance angiography. Int. J. Comput. Vis. 87(3), 235–265 (2010)

    Article  Google Scholar 

  20. He, Z., You, X., Zhou, L., Cheung, Y., Du, J.: Writer identification using fractal dimension of wavelet subbands in Gabor domain. Integr. Comput. Aided Eng. 17(2), 157–165 (2010)

    Article  Google Scholar 

  21. Wang, Y., Wang, X., Wan, W.: Object tracking with sparse representation and annealed particle filter. Signal Image Video Process. 8(6), 1059–1068 (2014)

    Article  Google Scholar 

  22. Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

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

  24. Keni, B., Rainer, S.: Evaluating multiple object tracking performance: the CLEAR MOT metrics. EURASIP J. Image Video Process. 2008, 1 (2008)

    Google Scholar 

  25. He, Z., Cui, Y., Wang, H., You, X., Chen, C.L.P.: One global optimization method in network flow model for multiple object tracking. Knowl. Based Syst 86, 21–32 (2015)

    Article  Google Scholar 

  26. Andriyenko A., Schindler, K.: Multi-target tracking by continuous energy minimization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1265–1272 (2011)

  27. Andriyenko, A., Schindler, K., Roth, S.: Discrete-continuous optimization for multi-target tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1926–1933 (2012)

  28. Dehghan, A., Tian, Y., Torr, P.H., Shah, M.: Target identity-aware network flow for online multiple target tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1146–1154 (2015)

  29. Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., Gool L.V.: Robust tracking-by-detection using a detector confidence particle filter. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1515–1522 (2009)

  30. Chari, V., Lacoste-Julien, S., Laptev, I., Sivic, J.: On pairwise costs for network flow multi-object tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5537–5545 (2015)

  31. Pirsiavash, H., Ramanan, D., Fowlkes, C.C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1201–1208 (2011)

  32. Bae, S.H., Yoon, K.J.: Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1218–1225 (2014)

  33. Milan, A., Roth, S., Schindler, K.: Continuous energy minimization for multitarget tracking. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 58–72 (2014)

    Article  Google Scholar 

  34. Milan, A., Leal-Taixé, L., Schindler, K., Reid, I.: Joint tracking and segmentation of multiple targets. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5397–5406 (2015)

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant Nos. 61672183, 61502119), by the Shenzhen Research Council (Grant Nos. JCYJ20170815113552036, JCYJ20170413104556946, JCYJ20160406161948211, JCYJ20160226201453085) and by the Natural Science Foundation of Guangdong Province (Grant No. 2015A030313544).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Yingyi Liang, Xiaohuan Lu & Zhenyu He

  2. School of Computer Science, Inner Mongolia University, Huhehot, China

    Yuan Zheng

Authors
  1. Yingyi Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaohuan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhenyu He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuan Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhenyu He.

Additional information

Publisher's Note

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

Yingyi Liang and Xiaohuan Lu contributed equally to this work and should be considered co-first authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, Y., Lu, X., He, Z. et al. Multiple object tracking by reliable tracklets. SIViP 13, 823–831 (2019). https://doi.org/10.1007/s11760-019-01418-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01418-3

Keywords

Search

Navigation