Skip to main content
SpringerLink
Log in

Abnormal event detection with semi-supervised sparse topic model

  • S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Most research on anomaly detection has focused on event that is different from its spatial–temporal neighboring events. However, it is still a significant challenge to detect anomalies that involve multiple normal events interacting in an unusual pattern. To address this problem, a novel semi-supervised method based on sparse topic model is proposed to detect anomalies in video surveillance. Short local trajectory method is used to extract motion information in order to improve the robustness of trajectories. For the purpose of strengthening the relationship of interest points on the same trajectory, the Fisher kernel method is applied to obtain the representation of trajectory which is quantized into visual word. Then, the sparse topic model is proposed to explore the latent motion patterns and achieve a sparse representation for the video scene. Finally, a semi-supervised learning method is applied to enhance the discrimination of model and improve the performance of anomaly detection. Experiments are conducted on QMUL dataset and AVSS dataset. The results demonstrated the superior efficiency of the proposed method.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Leyva R, Sanchez V, Li C (2015) Video anomaly detection based on wake motion descriptors and perspective grids. In: IEEE international workshop on information forensics and security. IEEE, pp 209–214

  2. Piciarelli C, Foresti GL (2010) Surveillance-oriented event detection in video streams. IEEE Intell Syst 26(3):32–41

    Article  Google Scholar 

  3. Cong Y, Yuan J, Liu J (2011) Sparse reconstruction cost for abnormal event detection. In: IEEE conference on computer vision and pattern recognition. IEEE Computer Society, pp 3449–3456

  4. Ren H, Moeslund TB (2014) Abnormal event detection using local sparse representation. In: IEEE international conference on advanced video and signal based surveillance. IEEE, pp 125–130

  5. Xia LM, Yang BJ, Tu HB (2015) Recognition of suspicious behavior using case-based reasoning. J Cent South Univ 22(1):241–250

    Article  Google Scholar 

  6. Roshtkhari MJ, Levine MD (2013) Online dominant and anomalous behavior detection in videos. In: Computer vision and pattern recognition. IEEE, pp 2611–2618

  7. Biswas S, Barur V (2015) Sparse representation based anomaly detection with enhanced local dictionaries. In: IEEE international conference on image processing. IEEE, pp 5532–5536

  8. Cui X, Liu Q, Gao M et al (2011) Abnormal detection using interaction energy potentials. In: Computer vision and pattern recognition. IEEE, pp 3161–3167

  9. Bera A, Kim S, Manocha D (2016) Realtime anomaly detection using trajectory-level crowd behavior learning. In: IEEE conference on computer vision and pattern recognition workshops. IEEE Computer Society, pp 1289–1296

  10. Cheng KW, Chen YT, Fang WH (2015) Gaussian process regression-based video anomaly detection and localization with hierarchical feature representation. IEEE Trans Image Process 24(12):5288–5301

    Article  MathSciNet  MATH  Google Scholar 

  11. Yang C, Yuan J, Ji L (2011) Sparse reconstruction cost for abnormal event detection. In: IEEE conference on computer vision & pattern recognition, pp 3449–3456

  12. Guo K, Ishwar P, Konrad J (2010) Action recognition using sparse representation on covariance manifolds of optical flow. In: IEEE international conference on advanced video & signal based surveillance. IEEE Computer Society, pp 188–195

  13. Mo X, Monga V, Bala R et al (2014) Adaptive sparse representations for video anomaly detection. IEEE Trans Circuits Syst Video Technol 24(4):631–645

    Article  Google Scholar 

  14. Kaviani R, Ahmadi P, Gholampour I (2014) Incorporating fully sparse topic models for abnormality detection in traffic videos. In: International conference on computer and knowledge engineering. IEEE, pp 586–591

  15. Emonet R, Varadarajan J, Odobez JM (2011) Extracting and locating temporal motifs in video scenes using a hierarchical non parametric bayesian model. In: Computer vision and pattern recognition. IEEE, pp 3233–3240

  16. Yoo Y, Yun K, Yun S et al (2016) Visual path prediction in complex scenes with crowded moving objects. In: Computer vision and pattern recognition. IEEE, pp 2668–2677

  17. Fu W, Wang J, Lu H et al (2013) Dynamic scene understanding by improved sparse topical coding. Pattern Recogn 46(7):1841–1850

    Article  Google Scholar 

  18. Wang J, Fu W, Lu H et al (2014) Bilayer sparse topic model for scene analysis in imbalanced surveillance videos. IEEE Trans Image Process 23(12):5198–5208

    Article  MathSciNet  MATH  Google Scholar 

  19. Jeong H, Yoo Y, Yi KM et al (2014) Two-stage online inference model for traffic pattern analysis and anomaly detection. Mach Vis Appl 25(6):1501–1517

    Article  Google Scholar 

  20. Pathak D, Sharang A, Mukerjee A (2015) Anomaly localization in topic-based analysis of surveillance videos. In: IEEE winter conference on applications of computer vision. IEEE Computer Society, pp 389–395

  21. Wang H, Klaser A, Schmid C et al (2013) Dense trajectories and motion boundary descriptors for action recognition. Int J Comput Vis 103(1):60–79

    Article  MathSciNet  Google Scholar 

  22. Perronnin F, Nchez J, Mensink T (2010) Improving the fisher kernel for large-scale image classification. In: European conference on computer vision. Springer, pp 143–156

  23. Perronnin F, Dance C, Csurka G et al (2006) Adapted vocabularies for generic visual categorization. In: European conference on computer vision. Springer, pp 464–475

  24. Eltoft T, Kim T, Lee TW (2006) On the multivariate Laplace distribution. IEEE Signal Process Lett 13(5):300–330

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 51678075), the Science and Technology Project of Hunan (Grant No. 2017GK2271), the Hunan Provincial Natural Science Foundation of China (Grant No.2017JJ2015).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, China

    Jun Wang, Limin Xia & Xiangjie Hu

  2. School of Information Management, Hunan University of Finance and Economics, Changsha, China

    Yongliang Xiao

Authors
  1. Jun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Limin Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiangjie Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongliang Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Limin Xia.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper (such as financial gain).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, J., Xia, L., Hu, X. et al. Abnormal event detection with semi-supervised sparse topic model. Neural Comput & Applic 31, 1607–1617 (2019). https://doi.org/10.1007/s00521-018-3417-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3417-1

Keywords

Search

Navigation