Advertisement

Turnstile Jumping Detection in Real-Time Video Surveillance

  • Huy Hoang NguyenEmail author
  • Thi Nhung Ta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11854)

Abstract

Turnstile jumping, a common action happening on a daily basis at high volume pedestrian areas, causes various problems for society. This study proposes a novel framework in detecting tunrstile jumping with no GPU necessary. The proposed model is a combination of a YOLO v2 based human detector, a Kernelized Correlation Filters (KCF) tracker and a Motion History Image (MHI)-based Convolutional Neural Network (CNN) classifier. Experimental results show that the developed model is not only capable of operating in real-time but can also detect suspicious human actions with an accuracy rate of 91.69%

Keywords

Abnormal human action Object detection CNN classification 

Notes

Acknowledgement

This research is funded by Ministry of Science and Technology of Vietnam (MOST) under grant number 10/2018/DTCTKC.01.14/16-20.

References

  1. 1.
  2. 2.
    Dhiman, C., Vishwakarma, D.K.: A review of state-of-the-art techniques for abnormal human activity recognition. Eng. Appl. Artif. Intell. 2, 21–45 (2019)CrossRefGoogle Scholar
  3. 3.
    Nanni, L., Ghidoni, S., Brahnam, S.: Handcrafted vs. non-handcrafted features for computer vision classification. Pattern Recognit. 71, 158–172 (2017)CrossRefGoogle Scholar
  4. 4.
    Chong, Y.S., Tay, Y.H.: Abnormal event detection in videos using spatiotemporal autoencoder. In: Cong, F., Leung, A., Wei, Q. (eds.) ISNN 2017. LNCS, vol. 10262, pp. 189–196. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-59081-3_23CrossRefGoogle Scholar
  5. 5.
    Cong, Y., Yuan, J., Liu, J.: Sparse reconstructioncost for abnormal event detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3449–3456. IEEE (2011)Google Scholar
  6. 6.
    Li, C., Han, Z., Ye, Q., Jiao, J.: Abnormal behavior detection via sparse reconstruction analysis of trajectory. In: International Conference on Image and Graphics, pp. 807–810. IEEE (2011)Google Scholar
  7. 7.
    Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 FPS in MATLAB. In: International Conference on Computer Vision, pp. 2720–2727. IEEE (2013)Google Scholar
  8. 8.
    Zhao, B., Fei-Fei, L., Xing, E.P.: Online detection of unusual events in videos via dynamic sparse coding. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3320–3323. IEEE (2011)Google Scholar
  9. 9.
    Tripathi, R.K., Jalal, A.S., Agrawal, S.C.: Suspicious human activity recognition: a review. Artif. Intell. Rev. 50(2), 283–339 (2018)CrossRefGoogle Scholar
  10. 10.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 511–518. IEEE (2001)Google Scholar
  11. 11.
    Dalal, N., Triggs, B.: Histogram of oriented gradients for human detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893. IEEE (2005)Google Scholar
  12. 12.
    Cruz, J.E.C., Shiguemori, E.H., Guimaraes, L.N.F.: A comparison of Haar-like, LBP and HOG approaches to concrete and asphalt runway detection in high resolution imagery. J. Comput. Interdisc. Sci. 6(3), 121–136 (2016)Google Scholar
  13. 13.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierachies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE (2014)Google Scholar
  14. 14.
    Girshick, R.: Fast R-CNN. In: International Conference on Computer Vision, pp. 1440–1448. IEEE (2015)Google Scholar
  15. 15.
    He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: International Conference on Computer Vision, pp. 2980–2988. IEEE (2017)Google Scholar
  16. 16.
    Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: Leibe, B., et al. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46448-0_2CrossRefGoogle Scholar
  17. 17.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Conference on Computer Vision and Pattern Recognition, pp. 779–788. IEEE (2016)Google Scholar
  18. 18.
    Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 257–267 (2001)CrossRefGoogle Scholar
  19. 19.
    Hsieh, C., Hsu, S.B., Han, C.C., Fan, K.C.: Abnormal event detection using trajectory features. J. Infer. Technol. Appl. 5(1), 22–27 (2011)Google Scholar
  20. 20.
    Tripathi, V., Gangodkar, D., Vivek, L., Mittal, A.: Robust abnormal event recognition via motion and shape analysis at ATM installations. J. Electr. Comput. Eng. 2015, 1–10 (2015)CrossRefGoogle Scholar
  21. 21.
    Foroughi, H., Aski, B.S., Pourreza, H.: Intelligent video surveillance for monitoring fall detection of elderly in home environments. In: International Conference on Computer and Information Technology, pp. 219–224. IEEE (2008)Google Scholar
  22. 22.
    Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Conference on Computer Vision and Pattern Recognition, pp. 6517–6525. IEEE (2017)Google Scholar
  23. 23.
    Huan, R., Pedoeem, J., Chen, C.: YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. In: Conference on Computer Vision and Pattern Recognition, pp. 2503–2510. IEEE (2018)Google Scholar
  24. 24.
  25. 25.
    João, F.H., Rui, C., Pedro, M., Jorge, B.: High-speed tracking with Kernelised correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2014)Google Scholar
  26. 26.
    Bobick, A.F., Davis, J.W.: Action recognition using temporal templates. In: Shah, M., Jain, R. (eds.) Motion-Based Recognition. Springer, Dordrecht (1997).  https://doi.org/10.1007/978-94-015-8935-2_6CrossRefGoogle Scholar
  27. 27.
    Caesar, H., Uijlings, J., Farrari, V.: COCO-stuff: thing and stuff classes in context. In: Conference on Computer Vision and Pattern Recognition, pp. 1209–1218. IEEE (2018)Google Scholar
  28. 28.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Electronics and TelecommunicationsHanoi University of Science and TechnologyHanoiVietnam

Personalised recommendations