The Journal of Supercomputing

, Volume 75, Issue 1, pp 189–196 | Cite as

Detection of centerline crossing in abnormal driving using CapsNet

  • Minjong Kim
  • Suyoung ChiEmail author


This paper presents the detection of centerline crossing in abnormal driving using a CapsNet. The benefit of the CapsNet is that the capsule contains all the data about the status of objects and recognizes objects as vectors; hence, these can be used to classify driving as normal or abnormal. The datasets use the Creative Commons Licenses from YouTube to obtain traffic accident footages and six time-flow images composed of data with our quantitative basis. A comparison of our proposed architecture with the CNN model showed that our method produces better results.


CapsNet Abnormal driving detection Deep convolutional neural network Dynamic routing between capsules 


  1. 1.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436CrossRefGoogle Scholar
  2. 2.
    Chen C, Seff A, Kornhauser A, Xiao J (2015) Deepdriving: learning affordance for direct perception in autonomous driving. In: 2015 IEEE International Conference on Computer Vision (ICCV), IEEE, pp 2722–2730Google Scholar
  3. 3.
    Tian Y, Luo P, Wang X, Tang X (2015) Deep learning strong parts for pedestrian detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1904–1912Google Scholar
  4. 4.
    Li J, Mei X, Prokhorov D, Tao D (2017) Deep neural network for structural prediction and lane detection in traffic scene. IEEE Trans Neural Netw Learn Syst 28(3):690–703CrossRefGoogle Scholar
  5. 5.
    Yu J, Chen Z, Zhu Y, Chen YJ, Kong L, Li M (2017) Fine-grained abnormal driving behaviors detection and identification with smartphones. IEEE Trans Mob Comput 16(8):2198–2212CrossRefGoogle Scholar
  6. 6.
    Chhabra R, Verma S, Krishna CR (2017) A survey on driver behavior detection techniques for intelligent transportation systems. In: 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence, IEEE, pp 36–41Google Scholar
  7. 7.
    Song JJ, Lee W (2017) Relevance maximization for high-recall retrieval problem: finding all needles in a haystack. J Supercomput 73:1–24CrossRefGoogle Scholar
  8. 8.
    Kim J, Lee W, Song JJ, Lee SB (2017) Optimized combinatorial clustering for stochastic processes. Clust Comput 20(2):1135–1148CrossRefGoogle Scholar
  9. 9.
    Cheng G, Wang Y, Xu S, Wang H, Xiang S, Pan C (2017) Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network. IEEE Trans Geosci Remote Sens 55(6):3322–3337CrossRefGoogle Scholar
  10. 10.
    Kerautret B, Krähenbühl A, Debled-Rennesson I, Lachaud JO (2016) Centerline detection on partial mesh scans by confidence vote in accumulation map. In: 2016 23rd International Conference on Pattern Recognition (ICPR), IEEE, pp 1376–1381Google Scholar
  11. 11.
    Hu J, Xu L, He X, Meng W (2017) Abnormal driving detection based on normalized driving behavior. IEEE Trans Veh Technol 66(8):6645–6652CrossRefGoogle Scholar
  12. 12.
    Zhang M, Chen C, Wo T, Xie T, Bhuiyan MZA, Lin X (2017) SafeDrive: online driving anomaly detection from large-scale vehicle data. IEEE Trans Ind Inform 13(4):2087–2096CrossRefGoogle Scholar
  13. 13.
    Shrestha D, Lovell DJ, Tripodis Y (2017) Hardware and software for collecting microscopic trajectory data on naturalistic driving behavior. J Intell Transp Syst 21(3):202–213CrossRefGoogle Scholar
  14. 14.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105Google Scholar
  15. 15.
    Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
  16. 16.
    Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Rabinovich A (2015) Going deeper with convolutions. CvprGoogle Scholar
  17. 17.
    Foster I, Kesselman C, Nick J, Tuecke S (2002) The physiology of the grid: an open grid services architecture for distributed systems integration. Technical report, Global Grid ForumGoogle Scholar
  18. 18.
    LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRefGoogle Scholar
  19. 19.
    Huang G, Liu Z, Weinberger KQ, van der Maaten L (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol 1, No 2, p 3Google Scholar
  20. 20.
    He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778Google Scholar
  21. 21.
    Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Advances in Neural Information Processing Systems, pp 3859–3869Google Scholar
  22. 22.
    YouTube Creative Commons License. Accessed 03 Jan 2018

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Korea University of Science and TechnologyDaejeonKorea
  2. 2.Electronics and Telecommunications Research InstituteDaejeonKorea

Personalised recommendations