Skip to main content
SpringerLink
Log in

An efficient framework for counting pedestrians crossing a line using low-cost devices: the benefits of distilling the knowledge in a neural network

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this paper, an efficient framework for counting pedestrians crossing a line of interest is proposed. Nowadays, the convolutional neural networks have very good results on pedestrian detection and tracking. However, the major drawback of the neural networks is that they require heavy computing resources. This limits the application of neural networks in low-cost systems. Thus, the low power consuming pedestrian counting systems with comparable performance are still important. To achieve this goal, the proposed method distils the pedestrian detection knowledge from a neural network to train the local binary patterns (LBP) cascade classifier model to detect pedestrians. Then a matching and tracking algorithm is used to count the number of pedestrians. An automaton was developed to eliminate the bouncing position of the detected pedestrians. The experimental comparisons show that, compared to Ma et al. and Felzenszwalb et al.’s methods, the quality of the line of interest counting of the proposed method is about the same and, at the same time, the execution time of the proposed method is much less.

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

Similar content being viewed by others

References

  1. Chan AB, Liang Z-SJ, Vasconcelos N (2008) Privacy preserving crowd monitoring: Counting people without people models or tracking. In: 2008 IEEE conference on computer vision and pattern recognition. IEEE, pp 1–7

  2. Cong Y, Gong H, Zhu S-C, Tang Y (2009) Flow mosaicking: Real-time pedestrian counting without scene-specific learning. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 1093–1100

  3. Dalal N, Triggs B (2005), Histograms of oriented gradients for human detection

  4. El-Shafie A-HA, Zaki M, Habib SE-D (2019) Fast cnn-based object tracking using localization layers and deep features interpolation, arXiv:1901.02620

  5. Fan D-P, Cheng M-M, Liu J-J, Gao S-H, Hou Q, Borji A (2018) Salient objects in clutter: Bringing salient object detection to the foreground. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 186–202

  6. Felzenszwalb P, McAllester D, Ramanan D (2008) A discriminatively trained, multiscale, deformable part model. In: 2008 IEEE conference on computer vision and pattern recognition. IEEE, pp 1–8

  7. Fu K, Zhao Q, Gu IY-H (2018) Refinet: a deep segmentation assisted refinement network for salient object detection. IEEE Trans Multimed 21:457–469

    Article  Google Scholar 

  8. Fu K, Zhao Q, Gu IY-H, Yang J (2019) Deepside: a general deep framework for salient object detection. Neurocomputing 356:69–82

    Article  Google Scholar 

  9. Han B, Sim J, Adam H (2017) Branchout: Regularization for online ensemble tracking with convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3356–3365

  10. Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network, arXiv:1503.02531

  11. Liao S, Zhu X, Lei Z, Zhang L, Li SZ (2007) Learning multi-scale block local binary patterns for face recognition. In: International conference on biometrics. Springer, pp 828–837

  12. Lienhart R, Maydt J (2002) An extended set of haar-like features for rapid object detection. In: Proceedings international conference on image processing, vol 1. IEEE, pp I–I

  13. Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection

  14. Ma Z, Chan AB (2015) Counting people crossing a line using integer programming and local features. IEEE Trans Circuits Syst Video Technol 26:1955–1969

    Article  Google Scholar 

  15. Ma Y, Chen W, Ma X, Xu J, Huang X, Maciejewski R, Tung AK (2017) Easysvm: a visual analysis approach for open-box support vector machines. Comput Vis Media 3:161–175

    Article  Google Scholar 

  16. Ma C, Tan T, Yang Q (2008) Cascade boosting lbp feature based classifiers for face recognition. In: 2008 3rd international conference on intelligent system and knowledge engineering, vol 1. IEEE, pp 1100–1104

  17. Nie G-Y, Cheng M-M, Liu Y, Liang Z, Fan D-P, Liu Y, Wang Y (2019) Multi-level context ultra-aggregation for stereo matching. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3283–3291

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

    Article  Google Scholar 

  19. Redmon J, Farhadi A (2018) Yolov3:, An incremental improvement, arXiv:1804.02767

  20. Schrier MJ, Puskorius G et al (2017) Pedestrian detection with saliency maps. US Patent App 14:997,120

    Google Scholar 

  21. Viola P, Jones M, et al. (2001) Rapid object detection using a boosted cascade of simple features. CVPR (1) 1:3

    Google Scholar 

  22. Zhao Z, Li H, Zhao R, Wang X (2016) Crossing-line crowd counting with two-phase deep neural networks. In: European conference on computer vision. Springer, pp 712–726

  23. Zhao J-X, Liu JJ, Fan D-P, Cao Y, Yang J, Cheng MM (2019) Egnet: Edge guidance network for salient object detection. In: Proceedings of the IEEE international conference on computer vision, pp 8779–8788

  24. Zheng H, Lin Z, Cen J, Wu Z, Zhao Y (2019) Cross-line pedestrian counting based on spatially-consistent two-stage local crowd density estimation and accumulation. IEEE Trans Circuits Syst Video Technol 29:787–799

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National PingTung University, No.4-18, Minsheng Rd., Pingtung, Taiwan

    Yih–Kai Lin, Chu–Fu Wang, Ching-Yu Chang & Hao–Lun Sun

Authors
  1. Yih–Kai Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chu–Fu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ching-Yu Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hao–Lun Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yih–Kai Lin.

Additional information

Publisher’s note

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

Supported by the Ministry of Science and Technology of Taiwan under contracts MOST-108-2221-E-153-005

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, Y., Wang, C., Chang, CY. et al. An efficient framework for counting pedestrians crossing a line using low-cost devices: the benefits of distilling the knowledge in a neural network. Multimed Tools Appl 80, 4037–4051 (2021). https://doi.org/10.1007/s11042-020-09276-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09276-9

Keywords

Search

Navigation