Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Robust real-time pedestrian detection in surveillance videos

  • 649 Accesses

  • 11 Citations

Abstract

Detecting different categories of objects in an image and video content is one of the fundamental tasks in computer vision research. Pedestrian detection is a hot research topic, with several applications including robotics, surveillance and automotive safety. We address the problem of detecting pedestrians in surveillance videos. In this paper, we present a new feature extraction method based on Multi-scale Center-symmetric Local Binary Pattern operator. All the modules (foreground segmentation, feature pyramid, training, occlusion handling) of our proposed method are introduced with its details about design and implementation. Experiments on CAVIAR and other sequences show that the presented system can detect pedestrians in real-time effectively and accurately in surveillance videos.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. Ahonen T, Hadid A, Pietikäinen M (2004) Face recognition with local binary patterns. In: Computer vision-eccv 2004. Springer, Berlin, Heidelberg, pp 469–481

  2. Benenson R, Omran M, Hosang J, Schiele B (2014) Ten years of pedestrian detection, what have we learned? In: Agapito L, Bronstein MM, Rother C (eds) Computer Vision-ECCV 2014 Workshops. Springer, pp 613–627

  3. Caviar (2007). http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/. Accessed 3 Aug 2015

  4. Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27

  5. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on IEEE, vol 1, pp 886–893

  6. Dollár P, Tu Z, Perona P, Belongie S (2009a) Integral channel features. In: BMVC, vol 2, p 5

  7. Dollár P, Wojek C, Schiele B, Perona P (2009b) Pedestrian detection: a benchmark. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, IEEE, pp 304–311

  8. Dollar P, Wojek C, Schiele B, Perona P (2012) Pedestrian detection: an evaluation of the state of the art. Pattern Anal Mach Intell IEEE Trans 34(4):743–761

  9. Enzweiler M, Gavrila DM (2009) Monocular pedestrian detection: survey and experiments. Pattern Anal Mach Intell IEEE Trans 31(12):2179–2195

  10. Ess A, Leibe B, Schindler K, Gool LV (2008) A mobile vision system for robust multi-person tracking. In: Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, IEEE, pp 1–8

  11. Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. Pattern Anal Mach Intell IEEE Trans 32(9):1627–1645

  12. Forsyth DA, Fleck MM (1997) Body plans. In: Computer Vision and Pattern Recognition, 1997. Proceedings, 1997 IEEE Computer Society Conference on IEEE, pp 678–683

  13. Gavrila DM (2007) A bayesian, exemplar-based approach to hierarchical shape matching. Pattern Anal Mach Intell IEEE Trans 29(8):1408–1421

  14. Guan YP (2010) Spatio-temporal motion-based foreground segmentation and shadow suppression. Computer Vision, IET 4(1):50–60

  15. Havasi L, Varga D, Szirányi T (2014) Lhi-tree: an efficientdisk-based image search application. In: Computational Intelligence for Multimedia Understanding (IWCIM), 2014 International Workshop on IEEE, pp 1–5

  16. Heikkilä M, Pietikäinen M, Schmid C (2006) Description of interest regions with center-symmetric local binary patterns. In: Computer vision, graphics and image processing. Springer, Berlin, Heidelberg, pp 58–69

  17. Hwang S, Park J, Kim N, Choi Y, Kweon IS (2013) Multispectral pedestrian detection: benchmark dataset and baseline. Integr Comput Aided Eng 20:347–360

  18. Leibe B, Seemann E, Schiele B (2005) Pedestrian detection in crowded scenes. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on IEEE, vol 1, pp 878–885

  19. Lin Z, Davis LS (2008) A pose-invariant descriptor for human detection and segmentation. In: Computer Vision–ECCV 2008. Springer, Berlin, Heidelberg, pp 423–436

  20. Lin Z, Davis LS (2010) Shape-based human detection and segmentation via hierarchical part-template matching. Pattern Anal Mach Intell IEEE Trans 32(4):604–618

  21. Maji S, Berg AC, Malik J (2008) Classification using intersection kernel support vector machines is efficient. In: Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, IEEE, pp 1–8

  22. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Anal Mach Intell IEEE Trans 24(7):971–987

  23. Papageorgiou C, Poggio T (2000) A trainable system for object detection. Int J Comput Vis 38(1):15–33

  24. Pietikäinen M (2005) Image analysis with local binary patterns. In: Image analysis. Springer, Berlin, Heidelberg, pp 115–118

  25. Seemann E, Leibe B, Schiele B (2006) Multi-aspect detection of articulated objects. In: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, IEEE, vol 2, pp 1582–1588

  26. Topi M, Timo O, Matti P, Maricor S (2000) Robust texture classification by subsets of local binary patterns. In: Pattern Recognition, 2000. Proceedings. 15th International Conference on, IEEE, vol 3, pp 935–938

  27. Varga D, Szirányi T, Kiss A, Sporás L, Havasi L (2015) A multi-view pedestrian tracking method in an uncalibrated camera network. In: Proceedings of the IEEE international conference on computer vision workshops, pp 37–44

  28. Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154

  29. Wojek C, Schiele B (2008) A performance evaluation of single and multi-feature people detection. In: Pattern recognition. Springer, pp 82–91

  30. Xu R, Guan Y, Huang Y (2015) Multiple human detection and tracking based on head detection for real-time video surveillance. Multimed Tools Appl 74(3):729–742

Download references

Acknowledgments

This work has been supported by the EU FP7 Programme (FP7-SEC-2011-1) No. 285320 (PROACTIVE project). The research was also partially supported by the Hungarian Scientific Research Fund (No. OTKA 106374).

Author information

Correspondence to Domonkos Varga.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Varga, D., Szirányi, T. Robust real-time pedestrian detection in surveillance videos. J Ambient Intell Human Comput 8, 79–85 (2017). https://doi.org/10.1007/s12652-016-0369-0

Download citation

Keywords

  • Video surveillance
  • Pedestrian detection
  • Feature extraction