Skip to main content
SpringerLink
Log in

Motorbike Counting in Heavily Crowded Scenes

  • Conference paper
  • First Online:
Future Data and Security Engineering (FDSE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13076))

Included in the following conference series:

Abstract

Vehicle density estimation has an important role in intelligent traffic systems. As of now, most established studies only focused on areas where people mainly travel by four-wheeled vehicles rather than motorbikes. However, in some countries such as Vietnam where motorbikes are the majority, traffic scenarios will pose different issues. Motorbikes are intrinsically more flexible so they can cause cluttered and chaotic visual. As a result, traffic video data captured in such environment is more challenging to existing systems. In this work, we performed an empirical survey on a set of vision-based counting methods covering a wide range of models and techniques. To our knowledge, there has not been many works dedicated to tackle this problem. Based on our experimental results, some of the top performers is ready to be used in real systems due to their robustness.

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 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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

References

  1. Amine Haoui, R.K., Varaiya, P.: Wireless magnetic sensors for traffic surveillance. Transp. Res. Part C: Emerg. Technol. 16(3), 294–306 (2008)

    Article  Google Scholar 

  2. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32

    Chapter  Google Scholar 

  3. Bosch, A., Zisserman, A., Muñoz, X.: Scene classification via pLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006). https://doi.org/10.1007/11744085_40

    Chapter  Google Scholar 

  4. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  5. Chan, A.B., Liang, Z.S., Vasconcelos, N.: Privacy preserving crowd monitoring: counting people without people models or tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–7. IEEE (2008)

    Google Scholar 

  6. Chan, A.B., Vasconcelos, N.: Counting people with low-level features and Bayesian regression. IEEE Trans. Image Process. 21(4), 2160–2177 (2012)

    Article  MathSciNet  Google Scholar 

  7. Chang, W.C., Cho, C.W.: Online boosting for vehicle detection. IEEE Trans. Syst. Man Cybern. B Cybern. 40(3), 892–902 (2010)

    Article  Google Scholar 

  8. Chen, B.H., Huang, S.C.: Probabilistic neural networks based moving vehicles extraction algorithm for intelligent traffic surveillance systems. Inf. Sci. 299, 283–295 (2015)

    Article  Google Scholar 

  9. Cherkassky, V., Ma, Y.: Selecting of the loss function for robust linear regression. Neural Comput. (2002)

    Google Scholar 

  10. Cho, S.Y., Chow, T.W., Leung, C.T.: A neural-based crowd estimation by hybrid global learning algorithm. IEEE Trans. Syst. Man Cybern. B Cybern. 29(4), 535–541 (1999)

    Article  Google Scholar 

  11. Conte, D., Foggia, P., Percannella, G., Tufano, F., Vento, M.: A method for counting moving people in video surveillance videos. EURASIP J. Adv. Signal Process. 2010, 5 (2010)

    Google Scholar 

  12. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

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

    Google Scholar 

  14. Dang, T.K., Pham, C.D., Nguyen, T.L.: A pragmatic elliptic curve cryptography-based extension for energy-efficient device-to-device communications in smart cities. Sustain. Cities Soc. 56, 102097 (2020)

    Article  Google Scholar 

  15. Dang, T.K., Pham, D.M.C., Ho, D.D.: On verifying the authenticity of e-commercial crawling data by a semi-crosschecking method. Int. J. Web Inf. Syst. (2019)

    Google Scholar 

  16. Dang, T.K., Tran, K.T.: The meeting of acquaintances: a cost-efficient authentication scheme for light-weight objects with transient trust level and plurality approach. Secur. Commun. Netw. 2019 (2019)

    Google Scholar 

  17. Dollár, P.: Piotr’s Computer Vision Matlab Toolbox (PMT) (2016). http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html

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

    Google Scholar 

  19. Foggia, P., Percannella, G., Sansone, C., Vento, M.: A graph-based algorithm for cluster detection. Int. J. Pattern Recogn. Artif. Intell. 22(05), 843–860 (2008)

    Article  Google Scholar 

  20. Foroughi, H., Ray, N., Zhang, H.: People counting with image retrieval using compressed sensing. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4354–4358. IEEE (2014)

    Google Scholar 

  21. Huynh, C.K., Dang, T.K., Le, T.S.: Motorbike detection in urban environment. In: Dang, T.K., Küng, J., Wagner, R., Thoai, N., Takizawa, M. (eds.) FDSE 2018. LNCS, vol. 11251, pp. 286–295. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03192-3_22

    Chapter  Google Scholar 

  22. Huynh, K.C., Thai, D.N., Le, S.T., Thoai, N., Hamamoto, K.: A robust method for estimating motorbike count based on visual information learning. In: Sixth International Conference on Graphic and Image Processing (ICGIP 2014), vol. 9443, p. 94431T. International Society for Optics and Photonics (2015)

    Google Scholar 

  23. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  24. Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, vol. 14, pp. 1137–1145 (1995)

    Google Scholar 

  25. Kong, D., Gray, D., Tao, H.: Counting pedestrians in crowds using viewpoint invariant training. In: BMVC. Citeseer (2005)

    Google Scholar 

  26. Lempitsky, V., Zisserman, A.: Learning to count objects in images. In: Advances in Neural Information Processing Systems, pp. 1324–1332 (2010)

    Google Scholar 

  27. Li, Y., Zhang, X., Chen, D.: CSRNet: dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091–1100 (2018)

    Google Scholar 

  28. Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099–5108 (2019)

    Google Scholar 

  29. Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157. IEEE (1999)

    Google Scholar 

  30. Ma, W., et al.: A wireless accelerometer-based automatic vehicle classification prototype system. IEEE Trans. Intell. Transp. Syst. 15(1), 104–111 (2014)

    Article  Google Scholar 

  31. MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, vol. 1, pp. 281–297 (1967)

    Google Scholar 

  32. Marana, A., Velastin, S., Costa, L., Lotufo, R.: Estimation of crowd density using image processing. In: IEE Colloquium on Image Processing for Security Applications (Digest No.: 1997/074), pp. 11–1. IET (1997)

    Google Scholar 

  33. Marana, A.N., da Fontoura Costa, L., Lotufo, R., Velastin, S.A.: Estimating crowd density with Minkowski fractal dimension. In: Proceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 6, pp. 3521–3524. IEEE (1999)

    Google Scholar 

  34. Marsden, M., McGuinness, K., Little, S., O’Connor, N.E.: Fully convolutional crowd counting on highly congested scenes. arXiv preprint arXiv:1612.00220 (2016)

  35. Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, pp. 8024–8035. Curran Associates, Inc. (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf

  36. Pelckmans, K., et al.: LS-SVMlab: a MATLAB/C toolbox for least squares support vector machines. Tutorial. KULeuven-ESAT. Leuven, Belgium (2002)

    Google Scholar 

  37. Rabaud, V., Belongie, S.: Counting crowded moving objects. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 705–711. IEEE (2006)

    Google Scholar 

  38. Rahmalan, H., Nixon, M.S., Carter, J.N.: On crowd density estimation for surveillance (2006)

    Google Scholar 

  39. Ryan, D., Denman, S., Fookes, C., Sridharan, S.: Crowd counting using multiple local features. In: Digital Image Computing: Techniques and Applications, DICTA 2009, pp. 81–88. IEEE (2009)

    Google Scholar 

  40. Sanaullah, I., Quddus, M., Enoch, M.: Developing travel time estimation methods using sparse GPS data. J. Intell. Transp. Syst. 20(6), 532–544 (2016)

    Article  Google Scholar 

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

  42. Suykens, J.A., et al.: Least Squares Support Vector Machines, vol. 4. World Scientific (2002)

    Google Scholar 

  43. Tang, Y., Zhang, C., Gu, R., Li, P., Yang, B.: Vehicle detection and recognition for intelligent traffic surveillance system. Multimed. Tools Appl. 76(4), 5817–5832 (2015). https://doi.org/10.1007/s11042-015-2520-x

    Article  Google Scholar 

  44. Vedaldi, A., Fulkerson, B.: VLFeat: an open and portable library of computer vision algorithms (2008). http://www.vlfeat.org/

  45. Wen, Q., Jia, C., Yu, Y., Chen, G., Yu, Z., Zhou, C.: People number estimation in the crowded scenes using texture analysis based on gabor filter. J. Comput. Inf. Syst. 7(11), 3754–3763 (2011)

    Google Scholar 

  46. Whitworth, R.: Ho Chi Minh City (Saigon), Vietnam Rush Hour Traffic in Real Time (2013). http://www.robwhitworth.co.uk/. Accessed 6 Jan 2016

  47. Wu, X., Liang, G., Lee, K.K., Xu, Y.: Crowd density estimation using texture analysis and learning. In: IEEE International Conference on Robotics and Biomimetics, ROBIO 2006. pp. 214–219. IEEE (2006)

    Google Scholar 

  48. Yaghoobi Ershadi, N., Menéndez, J.M.: Vehicle tracking and counting system in dusty weather with vibrating camera conditions. J. Sens. 2017 (2017)

    Google Scholar 

  49. Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th International Conference on World Wide Web, pp. 791–800. ACM (2009)

    Google Scholar 

Download references

Acknowledgements

Tran Khanh Dang is supported by a project with the Department of Science and Technology, Ho Chi Minh City, Vietnam (contract with HCMUT No. 42/2019/HD-QPTKHCN, dated 11/7/2019). We also thank all members of AC Lab and D-STAR Lab for their great supports and comments during the preparation of this paper.

Author information

Authors and Affiliations

  1. Department of Computer Science, Stony Brook University, New York, USA

    Chi Kien Huynh

  2. Ho Chi Minh City University of Technology, VNU-HCM, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  3. Social Insurance, Dong Nai Province, Ho Chi Minh City, Vietnam

    Cong An Nguyen

Authors
  1. Chi Kien Huynh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tran Khanh Dang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cong An Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tran Khanh Dang .

Editor information

Editors and Affiliations

  1. HCMC University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Suwon, Korea (Republic of)

    Tai M. Chung

  4. Hosei University, Koganei-shi, Tokyo, Japan

    Makoto Takizawa

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Huynh, C.K., Dang, T.K., Nguyen, C.A. (2021). Motorbike Counting in Heavily Crowded Scenes. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. FDSE 2021. Lecture Notes in Computer Science(), vol 13076. Springer, Cham. https://doi.org/10.1007/978-3-030-91387-8_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-91387-8_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91386-1

  • Online ISBN: 978-3-030-91387-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation