Skip to main content
SpringerLink
Log in

An Integrated Learning Framework for Pedestrian Tracking

  • Conference paper
  • First Online:
Intelligent Computing Methodologies (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10363))

Included in the following conference series:

  • 2469 Accesses

Abstract

Pedestrian tracking has been arguably addressed as a special topic beyond general object tracking. Although many learning or data driven object trackers as well as recent deep learning object trackers have shown excellent performance for general object tracking, they have limited success on pedestrian tracking because there exist three major challenges emerging from pedestrian tracking such as vast variations of human bodies, distraction from similar persons and complete occlusion. In this paper, we propose an integrated learning framework for pedestrian tracking to overcome these problems. It is demonstrated by the experimental results on the SVD-B dataset that our proposed framework can achieve competitive results in comparison with state-of-the-art object trackers under the evaluation of the precision and success rate as well as fps.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Notes

  1. 1.

    The source code and dataset are available at http://github.com/prinsphield/ILFPT.

References

  1. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409–1422 (2012)

    Article  Google Scholar 

  2. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)

    Article  Google Scholar 

  3. Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 254–265. Springer, Cham (2015). doi:10.1007/978-3-319-16181-5_18

    Google Scholar 

  4. Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Toor, P.H.: Staple: complementary learners for real-time tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1401–1409 (2016)

    Google Scholar 

  5. Hare, S., Saffari, A., Torr, P.H.: Struck: structured output tracking with kernels. In: International Conference on Computer Vision, pp. 263–270. IEEE (2011)

    Google Scholar 

  6. Danelljan, M., Robinson, A., Shahbaz Khan, F., Felsberg, M.: Beyond correlation filters: learning continuous convolution operators for visual tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 472–488. Springer, Cham (2016). doi:10.1007/978-3-319-46454-1_29

    Chapter  Google Scholar 

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

  8. Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: International Conference on Computer Vision (2015)

    Google Scholar 

  9. Ma, C., Huang, J., Yang, X., Yang, M.: Hierarchical convolutional features for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3074–3082 (2015)

    Google Scholar 

  10. Choi, J., Jin Chang, H., Jeong, J., Demiris, Y., Young Choi, J.: Visual tracking using attention-modulated disintegration and integration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4321–4330 (2016)

    Google Scholar 

  11. Gao, J., Ling, H., Hu, W., Xing, J.: Transfer learning based visual tracking with gaussian processes regression. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 188–203. Springer, Cham (2014). doi:10.1007/978-3-319-10578-9_13

    Google Scholar 

  12. Mei, X., Ling, H.: Robust visual tracking using ℓ1 minimization. In: International Conference on Computer Vision, pp. 1436–1443. IEEE (2009)

    Google Scholar 

  13. Han, B., Comaniciu, D., Zhu, Y., Davis, L.S.: Sequential kernel density approximation and its application to real-time visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1186–1197 (2008)

    Article  Google Scholar 

  14. Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1–3), 125–141 (2008)

    Article  Google Scholar 

  15. Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619–1632 (2011)

    Article  Google Scholar 

  16. Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: British Machine Vision Conference, vol. 1, p. 6 (2006)

    Google Scholar 

  17. Loy, C.C., Xiang, T., Gong, S.: Time-delayed correlation analysis for multi-camera activity understanding. Int. J. Comput. Vis. 90(1), 106–129 (2010)

    Article  Google Scholar 

  18. Lantagne, M., Parizeau, M., Bergevin, R.: VIP: vision tool for comparing images of people. In: Vision Interface, vol. 2 (2003)

    Google Scholar 

  19. Bazzani, L., Cristani, M., Murino, V.: Symmetry-driven accumulation of local features for human characterization and re-identification. Comput. Vis. Image Underst. 117(2), 130–144 (2013)

    Article  MATH  Google Scholar 

  20. Cheng, D.S., Cristani, M., Stoppa, M., Bazzani, M., Murino, V.: Custom pictorial structures for re-identification. In: British Machine Vision Conference, vol. 1, p. 6 (2011)

    Google Scholar 

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

    Article  Google Scholar 

  22. Prosser, B., Zheng, W., Gong, S., Xiang, T., Mary, Q.: Person re-identification by support vector ranking. In: British Machine Vision Conference, vol. 2, p. 6 (2010)

    Google Scholar 

  23. 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 

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

    Google Scholar 

  25. Li, W., Wang, X.: Locally aligned feature transforms across views. In: Computer Vision and Pattern Recognition, pp. 3594–3601 (2013)

    Google Scholar 

  26. Li, W., Zhao, R., Xiao, T., Wang, X.: DeepReID: deep filter pairing neural network for person re-identification. In: Computer Vision and Pattern Recognition, pp. 152–159 (2014)

    Google Scholar 

  27. Hirzer, M., Beleznai, C., Roth, P.M., Bischof, H.: Person re-identification by descriptive and discriminative classification. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 91–102. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21227-7_9

    Chapter  Google Scholar 

  28. Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: Proceedings of IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (PETS), vol. 3. Citeseer (2007)

    Google Scholar 

  29. Xiao, T., Li, H., Ouyang, W., Wang, X.: Learning deep feature representations with domain guided dropout for person re-identification. In: Computer Vision and Pattern Recognition (2016)

    Google Scholar 

  30. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)

    Google Scholar 

  31. Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: Computer Vision and Pattern Recognition, pp. 2411–2418 (2013)

    Google Scholar 

  32. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556

Download references

Acknowledgments

This work was supported by the Natural Science Foundation of China for Grant 61171138.

Author information

Authors and Affiliations

  1. Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China

    Taihong Xiao & Jinwen Ma

Authors
  1. Taihong Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinwen Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinwen Ma .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

  3. Inha University, Incheon, Korea (Republic of)

    Kyungsook Han

  4. Indian Institute of Technology Madras, Chennai, India

    M. Michael Gromiha

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Xiao, T., Ma, J. (2017). An Integrated Learning Framework for Pedestrian Tracking. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63315-2_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63314-5

  • Online ISBN: 978-3-319-63315-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation