Multimedia Tools and Applications

, Volume 78, Issue 5, pp 5819–5841 | Cite as

Automated camera sabotage detection for enhancing video surveillance systems

  • K. SitaraEmail author
  • B. M. Mehtre


Surveillance cameras are vital source of information in crime investigations. A surveillance video must be recorded with correct field of view and be of good quality, otherwise, it may not be suitable for investigation or analysis purposes. Perpetrators may tamper the recorded video or the physical device itself, in order to conceal their dubious activities. Generally, surveillance systems are unmanned due to limitations of manual monitoring. Automatic detection of camera tamper events is crucial for timely operator intervention. We propose a new method for detecting video camera tampering events like occlusion, defocus and displacement. The features used are edge information, frame count, foreground objects’ coverage area and its static nature. Effectiveness of our method is tested through experimentation on public datasets. The results obtained are encouraging with high detection and low false alarm rates. The proposed method automatically detects routine problems with cameras like dirt on camera lens, fog and smoke.


Camera tampering Camera sabotage Video surveillance Camera occlusion Camera defocus Camera displacement Secure systems Video forensics 


  1. 1.
    Ahmed SA, Dogra DP, Kar S, Kim BG, Hill P, Bhaskar H (2017) Localization of region of interest in surveillance scene. Multimed Tools Appl 76 (11):13651–13680. CrossRefGoogle Scholar
  2. 2.
    Aksay A, Temizel A, Enis Cetin A (2007) Camera tamper detection using wavelet analysis for video surveillance. In: IEEE Conference on Advanced Video and Signal Based Surveillance, AVSS 2007, pp 558–562.
  3. 3.
    Ashby MPJ (2017) The value of cctv surveillance cameras as an investigative tool: an empirical analysis. Eur J Crim Policy Res 23(3):441–459. CrossRefGoogle Scholar
  4. 4.
    Comiskey PM, Yarin AL, Attinger D (2017) High-speed video analysis of forward and backward spattered blood droplets. Forensic Sci Int 276(Supplement C):134–141. CrossRefGoogle Scholar
  5. 5.
    Ellwart D, Szczuko P, CzyŻewski A (2012) Camera sabotage detection for surveillance systems. In: Proceedings of the 2011 International Conference on Security and Intelligent Information Systems, SIIS’11. Springer-Verlag, Berlin, pp 45–53CrossRefGoogle Scholar
  6. 6.
    Fischler MA, Bolles RC (1981) Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395. MathSciNetCrossRefGoogle Scholar
  7. 7.
    Fitzsimons J, Dawson-Howe K (2016) Abandoned, removed and moved object classification. Int J Pattern Recogn Artif Intell 30(01):1655002. MathSciNetCrossRefGoogle Scholar
  8. 8.
    Gil-Jiménez P, López-Sastre R, Siegmann P, Acevedo-Rodríguez J, Maldonado-Bascón S (2007) Automatic control of video surveillance camera sabotage. In: Proceedings of the 2nd International Work-conference on Nature Inspired Problem-692 Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II, IWINAC’07. Springer-Verlag, Berlin, pp 222–231
  9. 9.
    Huang DY, Chen CH, Chen TY, Hu WC, Chen BC (2014) Rapid detection of camera tampering and abnormal disturbance for video surveillance system. J Vis Commun Image Represent 25(8):1865–1877. CrossRefGoogle Scholar
  10. 10.
    Jerian M, Paolino S, Cervelli F, Carrato S, Mattei A, Garofano L (2007) A forensic image processing environment for investigation of surveillance video. Forensic Sci Int 167(2):207–212. Selected Articles of the 4th European Academy of Forensic Science Conference (EAFS2006), 2006 Helsinki, Finland. CrossRefGoogle Scholar
  11. 11.
    Kryjak T, Komorkiewicz M, Gorgon M (2012) FPGA implementation of camera tamper detection in real-time. In: Conference on Design and Architectures for Signal and Image Processing (DASIP), 2012, pp 1–8Google Scholar
  12. 12.
    Lavee G, Khan L, Thuraisingham B (2007) A framework for a video analysis tool for suspicious event detection. Multimed Tools Appl 35(1):109–123. CrossRefGoogle Scholar
  13. 13.
    Lee J, Lee ED, Tark HO, Hwang JW, Yoon DY (2008) Efficient height measurement method of surveillance camera image. Forensic Sci Int 177(1):17–23. CrossRefGoogle Scholar
  14. 14.
    Li N, Wu X, Guo H, Xu D, Ou Y, Chen YL (2015) Anomaly detection in video surveillance via gaussian process. Int J Pattern Recogn Artif Intell 29(06):1555011. CrossRefGoogle Scholar
  15. 15.
    Lin DT, Wu CH (2012) Real-time active tampering detection of surveillance camera and implementation on digital signal processor. In: Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 2012, pp 383–386.
  16. 16.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110. CrossRefGoogle Scholar
  17. 17.
    Milani S, Fontani M, Bestagini P, Barni M, Piva A, Tagliasacchi M, Tubaro S (2012) An overview on video forensics. APSIPA Trans Signal Inf Process e2:1Google Scholar
  18. 18.
    Muja M, Lowe DG (2009) Fast approximate nearest neighbors with automatic algorithm configuration. In: International Conference on Computer Vision Theory and Application vissapp’09). INSTICC press, pp 331–340Google Scholar
  19. 19.
    Ramstrand N, Ramstrand S, Brolund P, Norell K, Bergström P (2011) Relative effects of posture and activity on human height estimation from surveillance footage. Forensic Sci Int 212(1):27–31. CrossRefGoogle Scholar
  20. 20.
    Ribnick E, Atev S, Masoud O, Papanikolopoulos N, Voyles R (2006) Real-time detection of camera tampering. In: Proceedings of the IEEE International Conference on Video and Signal Based Surveillance, AVSS’06. IEEE Computer Society, Washington, pp 10–15
  21. 21.
    Russo P, Gualdi-Russo E, Pellegrinelli A, Balboni J, Furini A (2017) A new approach to obtain metric data from video surveillance: Preliminary evaluation of a low-cost stereo-photogrammetric system. Forensic Sci Int 271(Supplement C):59–67. CrossRefGoogle Scholar
  22. 22.
    Saglam A, Temizel A (2009) Real-time adaptive camera tamper detection for video surveillance. In: Sixth IEEE international conference on advanced video and signal based surveillance, 2009. AVSS ’09., pp 430–435.
  23. 23.
    Shih CC, Chen SC, Hung CF, Chen KW, Lin SY, Lin CW, Hung YP (2013) Real-time camera tampering detection using two-stage scene matching. In: 2013 IEEE international conference on multimedia and expo (ICME), pp 1–6.
  24. 24.
    Sitara K, Mehtre BM (2016) Digital video tampering detection: An overview of passive techniques. Digit Investig 18:8–22. CrossRefGoogle Scholar
  25. 25.
    Sitara K, Mehtre BM (2016) Real-time automatic camera sabotage detection for surveillance systems. In: Advances in Signal Processing and Intelligent Recognition Systems. Springer, p 75–84. Google Scholar
  26. 26.
    Sitara K, Mehtre BM (2017) A comprehensive approach for exposing inter-frame video forgeries. In: 2017 IEEE 13th International Colloquium on Signal Processing its Applications (CSPA), pp 73–78.
  27. 27.
    Stamm MC, Lin WS, Liu KJR (2012) Temporal forensics and anti-forensics for motion compensated video. IEEE Trans Inform Forensic Secur 7(4):1315–1329. CrossRefGoogle Scholar
  28. 28.
    Su PC, Suei PL, Chang MK, Lain J (2015) Forensic and anti-forensic techniques for video shot editing in H.264/AVC. J Vis Commun Image Represent 29:103–113. CrossRefGoogle Scholar
  29. 29.
    Sulman N, Sanocki T, Goldgof D, Kasturi R (2008) How effective is human video surveillance performance? In: 2008 19th International Conference on Pattern Recognition, pp 1–3.
  30. 30.
    Szeliski R (1996) Video mosaics for virtual environments. IEEE Comput Graph Appl 16(2):22–30. CrossRefGoogle Scholar
  31. 31.
    Tsesmelis T, Christensen L, Fihl P, Moeslund TB (2013) Tamper detection for active surveillance systems. In: 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (avss), pp 57–62.
  32. 32.
    Tung CL, Tung PL, Kuo CW (2012) Camera tamper detection using codebook model for video surveillance. In: International Conference on Machine Learning and Cybernetics (icmlc), 2012, vol 5, pp 1760–1763.
  33. 33.
    Vedaldi A, Fulkerson B (2008) Vlfeat An open and portable library of computer vision algorithms
  34. 34.
    Wang YK, Fan CT, Cheng KY, Deng PS (2011) Real-time camera anomaly detection for real-world video surveillance. In: International Conference on Machine Learning and Cybernetics (ICMLC), 2011, vol 4, pp 1520–1525.
  35. 35.
    Yin H, Jiao X, Luo X, Yi C (2013) Sift-based camera tamper detection for video surveillance. In: 25th Chinese Control and Decision Conference (CCDC), 2013, pp 665–668.
  36. 36.
    Zhang T, Yang Z, Jia W, Yang B, Yang J, He X (2016) A new method for violence detection in surveillance scenes. Multimed Tools Appl 75(12):7327–7349. CrossRefGoogle Scholar

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

Authors and Affiliations

  1. 1.Center of Excellence in Cyber SecurityInstitute for Development and Research in Banking Technology (IDRBT), Established by Reserve Bank of India (RBI)HyderabadIndia
  2. 2.School of Computer and Information SciencesUniversity of HyderabadHyderabadIndia

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