Skip to main content
SpringerLink
Log in

Suspicious Event Detection in Real-Time Video Surveillance System

  • Conference paper
  • First Online:
Social Networking and Computational Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 100))

Abstract

In today’s generation, video security is becoming more important in the real-world applications because of the happening of suspicious events in our surroundings, and the safety and security in public places have become a priority. Video surveillance system might be used for enhancing the security in various areas such as offices, mall, theater, organizations, analysis of athletic events, content-based image storage and retrieval and many more. This paper focused research on the automatic analysis of suspicious event detection in the real-time video surveillance system and provided a recommendation on how it can be monitored automatically.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Popoola OP, Wang K (2012) Video-based abnormal human behavior recognition-A review. IEEE Trans Syst Man Cybernetics Part C Appl Rev 42(6):865–878

    Article  Google Scholar 

  2. Krcadinac U, Pasquier P, Jovanovic J, Devedzic V (2013) Synesketch: an open source library for sentence-based emotion recognition. IEEE Trans Affect Comput 4(3):312–325

    Article  Google Scholar 

  3. Candamo J, Shreve M, Goldgof DB, Sapper DB, Kasturi R (2010) Understanding transit scenes: a survey on human behavior-recognition algorithms. IEEE Trans Intell Transp Syst 11(1):206–224

    Article  Google Scholar 

  4. Kalaiselvan C, SivananthaRaja A (2012) Investigation on tracking system for real-time video surveillance applications. In: CUBE 2012, ACM, Pune, Maharashtra, India, pp 108–112

    Google Scholar 

  5. Kao LJ, Huang YP (2011) An efficient strategy to detect outlier transactions for knowledge mining. IEEE, pp 2670–2675

    Google Scholar 

  6. Li Y, Wu Z, Karanam S, Radke RJ (2014) Real-world re-identification in an airport camera network. ACM, ICDSC’14, Venezia Mestre, Italy

    Google Scholar 

  7. Liu H, Schneider M (2011) Tracking continuous topological changes of complex moving regions. ACM, SAC’11, Taichung, Taiwan, pp 833–838

    Google Scholar 

  8. Karasulu B, Korukoglu S (2013) Moving object detection and tracking in videos. Springer, Performance Evaluation Software, pp 7–30

    Chapter  Google Scholar 

  9. Tian Y, Feris RS, Liu H, Hampapur A, Sun MT (2011) Robust detection of abandoned and removed objects in complex surveillance videos. IEEE Trans Syst Man Cybernetics Part C Appl Rev 41(5):565–576

    Article  Google Scholar 

  10. Thomas V, Ray AK (2011) Fuzzy particle filter for video surveillance. IEEE Trans Fuzzy Syst 19(5):937–945

    Article  Google Scholar 

  11. Chen YL, Wu BF, Huang HY, Fan CJ (2011) A real-time vision system for nighttime vehicle detection and traffic surveillance. IEEE Trans Ind Electron 58(5):2030–2044

    Article  Google Scholar 

  12. García J, Gardel A, Bravo I, Lázaro JL, Martínez M (2013) Tracking people motion based on extended condensation algorithm. IEEE Trans Syst Man Cybern Syst 43(3):606–618

    Article  Google Scholar 

  13. Ahmad I, He Z, Sinica A, Sun MT (2008) Special issue on video surveillance. IEEE Trans Circuits Syst Video Technol 18(8):1001–1005

    Article  Google Scholar 

  14. Chao L, Tao J, Yang M, Li Y, Wen Z (2014) Multi-scale temporal modeling for dimensional emotion recognition in video. ACM, AVEC’14, Orlando, Florida, USA, pp 11–18

    Google Scholar 

  15. Krishna T, Rai A, Bansal S, Khandelwal S, Gupta S, Goyal D (2013) Emotion recognition using facial and audio features. ACM, ICMI’13, Sydney, Australia, December 9–13

    Google Scholar 

  16. Poria S, Cambria E, Howard N, Huang GB, Hussain A (2015) Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Elsevier, Neurocomputing 50–59

    Google Scholar 

  17. El Meguid MKA, Levine MD (2014) Fully automated recognition of spontaneous facial expressions in videos using random forest classifiers. IEEE Trans Affect Comput 5(2):141–154

    Article  Google Scholar 

  18. Ye Y, Nurmi P (2015) Gestimator—shape and stroke similarity based gesture recognition. ACM, ICMI 2015, Seattle, WA, USA, pp 219–226

    Google Scholar 

  19. Zhang P, Thomas T, Emmanuel S, Kankanhalli MS (2010) Privacy-preserving video surveillance using pedestrian tracking mechanism. ACM, MiFOR’10, Firenze, Italy, pp 31–36

    Google Scholar 

  20. Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41(3):1–58

    Article  Google Scholar 

  21. Verma KK, Kumar P, Tomar A (2015) Analysis of moving object detection and tracking in video surveillance system. In: 2nd international conference on computing for sustainable global development, IEEE, pp 1758–1762

    Google Scholar 

  22. Little S, Clawson K, Nieto M (2013) An information retrieval approach to identifying infrequent events in surveillance video. ACM, ICMR’13, Dallas, Texas, USA., 16–20 April 2013, pp 223–230

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. UIT, RGPV, Bhopal, Madhya Pradesh, India

    Madhuri Agrawal & Shikha Agrawal

Authors
  1. Madhuri Agrawal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shikha Agrawal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Madhuri Agrawal or Shikha Agrawal .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Sagar Institute of Research and Technology, Bhopal, Madhya Pradesh, India

    Rajesh Kumar Shukla

  2. Department of Computer Science and Engineering, University Teaching Department, Rajiv Gandhi Technical University (State Technological University), Bhopal, Madhya Pradesh, India

    Jitendra Agrawal

  3. School of Information Technology, Rajiv Gandhi Technical University (State Technological University), Bhopal, Madhya Pradesh, India

    Sanjeev Sharma

  4. Department of Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Narendra S. Chaudhari

  5. Department of Computer Science and Engineering, Indian Institute of Technology BHU, Varanasi, Uttar Pradesh, India

    K. K. Shukla

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Agrawal, M., Agrawal, S. (2020). Suspicious Event Detection in Real-Time Video Surveillance System. In: Shukla, R., Agrawal, J., Sharma, S., Chaudhari, N., Shukla, K. (eds) Social Networking and Computational Intelligence. Lecture Notes in Networks and Systems, vol 100. Springer, Singapore. https://doi.org/10.1007/978-981-15-2071-6_40

Download citation

Publish with us

Policies and ethics

Search

Navigation