Skip to main content
SpringerLink
Log in

Video crowd detection and abnormal behavior model detection based on machine learning method

  • S.I. : Machine Learning Applications for Self-Organized Wireless Networks
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Pedestrian detection and abnormal behavior detection is the computer for a given image and video, to determine whether there are pedestrians and their behavior is normal. Pedestrian detection is the basis and premise of pedestrian tracking, behavior analysis, gait analysis, pedestrian identity recognition and so on. A good pedestrian detection algorithm can provide strong support and guarantee for the latter. The overall goal of this project is to learn different data mining methods and try to improve the detection accuracy of video crowd machine abnormal behavior. Aiming at the shortage of user behavior anomaly detection model proposed by Lane et al., a new IDS anomaly detection model is proposed. This model improves the representation of user behavior patterns and behavior profiles and adopts a new similarity assignment method. Experiments based on Unix user shell command data show that the detection model proposed in this paper has higher detection performance.

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
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Mohammadi S, Galoogahi HK, Perina A, Murino V (2017) Physics-inspired models for detecting abnormal behaviors in crowded scenes. Group Crowd Behav Comput Vis 1(12):253–272

    Article  Google Scholar 

  2. Rabiee H, Mousavi H, Nabi M, Ravanbakhsh M (2017) Detection and localization of crowd behavior using a novel tracklet-based model. Int J Mach Learn Cybern 2(2):1–12

    Google Scholar 

  3. Wang X, Gao M, He X et al (2014) An abnormal crowd behavior detection algorithm based on fluid mechanics. J Comput 9(5):1144–1149

    Google Scholar 

  4. Cui J, Liu W, Xing W (2014) Crowd behaviors analysis and abnormal detection based on surveillance data. J Vis Lang Comput 25(6):628–636

    Article  Google Scholar 

  5. Roubtsova ANS, With PHND (2013) Group localisation and unsupervised detection and classification of basic crowd behaviour events for surveillance applications. Proc SPIE Int Soc Opt Eng 8663(3):135–145

    Google Scholar 

  6. Zhang X, Wang M, Zuo J et al (2015) Abnormal crowd behavior detection based on motion clustering of mesoscopic group. Yi Qi Yi Biao Xue Bao/Chin J Sci Instrum 36(5):1106–1114

    Google Scholar 

  7. Abardeig J, Cai J, Zhu Z (2016) Study on the method of detecting the crowd abnormality in the sensitive media image based on behavior analysis. Recent Adv Electr Electron Eng 9(1):29–33

    Google Scholar 

  8. Zhu S, Hu J, Shi Z (2016) Local abnormal behavior detection based on optical flow and spatio-temporal gradient. Multimed Tools Appl 75(15):9445–9459

    Article  Google Scholar 

  9. Alvar M, Torsello A, Sanchez-Miralles A et al (2014) Abnormal behavior detection using dominant sets. Mach Vis Appl 25(5):1351–1368

    Article  Google Scholar 

  10. Zhang X, Zhang Q, Hu S et al (2018) Energy level-based abnormal crowd behavior detection. Sensors 18(2):423

    Article  Google Scholar 

  11. Chan YT (2017) Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems. J Electron Imaging 26(5):051402

    Article  Google Scholar 

  12. Balasubramanian Y (2015) Human crowd behavior analysis based on graph modeling and matching in a synoptic video. Volume 3(3):1050–1056

    Google Scholar 

  13. Zhao F, Li J (2014) Pedestrian motion tracking and crowd abnormal behavior detection based on intelligent video surveillance. J Netw 9(10):2598

    Google Scholar 

  14. Sang HF, Yu C, Da-HE K (2016) Crowd gathering and running behavior detection based on overall features. J Optoelectron Laser 27(1):52–60

    Google Scholar 

  15. Pan S, Sun W, Zheng Z (2017) Video segmentation algorithm based on superpixel link weight model. Multimed Tools Appl 76(19):19741–19760

    Article  Google Scholar 

  16. Shehab A, Elhoseny M, Muhammad K, Sangaiah AK, Yang P, Huang H, Hou G (2018) Secure and robust fragile watermarking scheme for medical images. IEEE Access 6(1):10269–10278

    Article  Google Scholar 

  17. Zheng Z, Huang T, Zhang H et al (2016) Towards a resource migration method in cloud computing based on node failure rule. J Intell Fuzzy Syst 31(5):2611–2618

    Article  Google Scholar 

  18. Tharwat A, Mahdi H, Elhoseny M, Hassanien AE (2018) Recognizing human activity in mobile crowdsensing environment using optimized k-NN algorithm. Expert Syst Appl 107(1):32–44

    Article  Google Scholar 

  19. Zheng Z, Jeong HY, Huang T et al (2017) KDE based outlier detection on distributed data streams in multimedia network. Multimed Tools Appl 76(17):18027–18045

    Article  Google Scholar 

Download references

Acknowledgements

This paper is supported by the Science and Technology Research Project of Chongqing Municipal Education Committee (Grant: KJ1704089).

Author information

Authors and Affiliations

  1. School of Big Data and Software Engineering, Chongqing University, Chongqing, 400030, China

    Shaoci Xie & Xiaohong Zhang

  2. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Jing Cai

Authors
  1. Shaoci Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaohong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shaoci Xie.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, S., Zhang, X. & Cai, J. Video crowd detection and abnormal behavior model detection based on machine learning method. Neural Comput & Applic 31 (Suppl 1), 175–184 (2019). https://doi.org/10.1007/s00521-018-3692-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3692-x

Keywords

Search

Navigation