Skip to main content
SpringerLink
Log in

The future of video analytics for surveillance and its ethical implications

  • Original Article
  • Published:
Security Journal Aims and scope Submit manuscript

Abstract

The current state of the art and direction of research in computer vision aimed at automating the analysis of CCTV images is presented. This includes low level identification of objects within the field of view of cameras, following those objects over time and between cameras, and the interpretation of those objects’ appearance and movements with respect to models of behaviour (and therefore intentions inferred). The potential ethical problems (and some potential opportunities) such developments may pose if and when deployed in the real world are presented, and suggestions made as to the necessary new regulations which will be needed if such systems are not to further enhance the power of the surveillers against the surveilled.

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

Similar content being viewed by others

Notes

  1. That is, violations would not automatically be grounds for a criminal or civil conviction.

  2. Public, semi-public, semi-private and private spaces are defined in Adams (2007).

References

  • Adams, A.A. (2007) Regulating CCTV. Pages 3–14 of: Ethicomp 2007: Proceedings of the Ninth International Conference. Tokyo, Japan: Meiji University.

    Google Scholar 

  • Adams, A.A., Murata, K. and Orito, Y. (2010) The development of Japanese data protection. Policy and Internet 2 (2): 93–124.

    Article  Google Scholar 

  • Annesley, J., Colombo, A., Orwell, J. and Velastin, S.A. (2007) A profile of MPEG-7 for visual surveillance. In: Proceedings of the Fourth IEEE Advanced Video and Signal-Based Surveillance (AVSS), London, IEEE Computer Society: Online, doi:10.1109/AVSS.2007.4425358, pp. 482–487.

  • BBC. (2003) (February). Pensioner freed after FBI bungle, news.bbc.co.uk/2/hi/uk_news/england/2799791.stm.

  • BBC. (2006) (January). Peeping tom CCTV workers jailed, news.bbc.co.uk/2/hi/uk_news/england/merseyside/4609746.stm.

  • BBC. (2012) High-def CCTV cameras risk backlash, warns UK watchdog, 3 October, www.bbc.co.uk/news/technology-19812385.

  • Bazzani, L., Cristani, M., Perina, M. and Murino, V. (2012) Multiple-shot person re-identification by chromatic and epitomic analyses. Pattern Recognition Letters 33 (7): 898–903.

    Article  Google Scholar 

  • Ben Shitrit, H., Berclaz, J., Fleuret, F. and Fua, P. (2011) Tracking multiple people under global appearance constraints. In: D.N. Metaxas, L. Quan, A. Sanfeliu and L.J. Van Gool (eds.) 2011 IEEE International Conference On, Computer Vision (ICCV), Barcelona. IEEE Computer Society, Online, doi:10.1109/ICCV.2011.6126235, pp. 137–144.

  • Berger, A.M. (2000) Privacy mode for acquisition cameras and camcorders. US Patent 6067399, Sony Corporation, 23 May.

  • Brin, D. (1998) The Transparent Society. Jackson, TN: Perseus.

    Google Scholar 

  • Cezar, J., Jacques, S. and Musse, S.R. (2010) Crowd analysis using computer vision techniques. IEEE Signal Processing Magazine 27 (September): 66–77.

    Google Scholar 

  • Coetzer, B., van der Merwe, J. and Josephs, B. (2011) Information management and video analytics: The future of intelligent video surveillance. In: W. Lin (ed.) Video Surveillance. Rijeka, Croatia: InTech, pp. 3–16.

  • Coudert, F., Gemo, M., Beslay, L. and Andritsos, F. (2011) Pervasive monitoring: Appreciating citizen's surveillance as digital evidence in legal proceedings. Imaging for Crime Detection and Prevention (2011) (ICDP 2011), 4th International Conference On, London. IEEE Computer Society, Online, doi:10.1049/ic.2011.0130, pp. 1–6.

  • Dee, H. and Hogg, D. (2004) Detecting inexplicable behaviour. In: A. Hoppe, S. Barman and T. Ellis (eds). Proceedings of the British Machine Vision Conference, The British Machine Vision Association, Kingston, London. British Machine Vision Association (BMVA), pp. 73–81.

  • Dee, H.M. and Hogg, D.C. (2009) Navigational strategies in behaviour modelling. Artificial Intelligence 173 (2): 329–342.

  • Dee, H.M. and Velastin, S.A (2008) How close are we to solving the problem of automated visual surveillance? Machine Vision and Applications: 19 (5–6): 329–343.

    Article  Google Scholar 

  • Dick, A.R. and Brooks, M.J. (2003) Issues in automated visual surveillance. In: C. Sun, H. Talbot, S. Ourselin and T . Adriaansen (eds.). Proceedings 7th International Conference on Digital Image Computing: Techniques and Applications, DICTA'03. Macquarie University, Sydney, Australia. CSIRO Publishing, pp. 195–204, 16–12 December.

  • Dollár, P., Wojek, C., Schiele, B. and Perona, P. (2012) Pedestrian detection: An evaluation of the state of the art. Pattern Analysis and Machine Intelligence (PAMI) 34 (4): 743–761.

    Article  Google Scholar 

  • Donald, C. (2005) How many monitors should a CCTV operator view? CCTV Image. Spring, p. 35.

    Google Scholar 

  • Dufaux, F. and Ebrahimi, T. (2008) Scrambling for privacy protection in video surveillance systems. IEEE Transactions on Circuits and Systems for Video Technology 18 (8): 1168–1174.

    Article  Google Scholar 

  • Evans, M., Boyle, J.N. and Ferryman, J. (2012) Vehicle classification using evolutionary forests. In: P.L. Carmona, J.S. Sánchez and A.L.N. Fred (eds.) Proceedings of the International Conference on Pattern Recognition Applications and Methods, Vilamoura Portugal. SciTe Press, pp. 387–393.

  • Florence, J. (2006) Making the no fly list fly: A due process model for terrorist watchlists. Yale Law Journal 115 (8): 2148–2181.

    Article  Google Scholar 

  • Gong, S., Loy, C.C. and Xiang, T. (2011) Security and Surveillance. New York: Springer.

    Book  Google Scholar 

  • Goold, B.J. (2002) Privacy rights and public spaces: CCTV and the problem of the ‘Unobservable observer’. Criminal Justice Ethics 21 (1): 21–27.

    Article  Google Scholar 

  • Helbing, D. and Molnar, P. (1995) Social force model for pedestrian dynamics. Physical Review E 51 (5): 4282–4286.

    Article  Google Scholar 

  • Hempel, L. and Töpfer, E. (2004) CCTV in Europe (Final Report). Technical Report, Urban Eye Project: HPSE-CT2001-00094.

  • Hu, W., Tan, T., Wang, L. and Maybank, S (2004) A survey on visual surveillance of object motions and behaviour. IEEE Transactions on Systems, Man, and Cybernetics 34 (3): 334–252.

    Article  Google Scholar 

  • Independent Police Complaints Commission (UK). (2007) Stockwell One: Investigation into the shooting of Jean Charles de Menezes at Stockwell Underground Station on 22 July 2005, 8 November.

  • Introna, L.D. (2000) Workplace surveillance, privacy and distributive justice. ACM SIGCAS Computers and Society 30 (4): 33–39.

    Article  Google Scholar 

  • Introna, L.D. and Wood, D. (2004) Picturing algorithmic surveillance: The politics of facial recognition systems. Surveillance and Society 2 (2/3): 177–198.

    Google Scholar 

  • Jia, K. and Gong, S. (2008) Generalized face super-resolution. IEEE Transactions on Image Processing 17 (6): 873–886.

    Article  Google Scholar 

  • Ko, T. (2011) Video surveillance. InTech. Chap. A survey on behaviour analysis in video surveillance applications. http://tinyurl.com/8hlveuk.

  • Lee, S.-W., Park, J. and Lee, S.-W. (2006) Low resolution face recognition based on support vector data description. Pattern Recognition 39 (9): 1809–1812.

    Article  Google Scholar 

  • Lewis, P. (2009) Ian Tomlinson death: Guardian video reveals police attack on man who died at G20 protest. The Guardian, 7 April. www.guardian.co.uk/uk/2009/apr/07/ian-tomlinson-g20-death-video.

  • Liu, C., Shum, H.-Y. and Freeman, W.T. (2007) Face hallucination: Theory and practice. International Journal of Computer Vision 75 (1): 115–134.

    Article  Google Scholar 

  • Lyall, S. (2009) Critics assail British Police for harsh tactics during the G-20 Summit Meeting. New York Times, 30 May. www.nytimes.com/2009/05/31/world/europe/31police.html.

  • MarketResearch.com. (2011) Global video surveillance market, applications and management services forecasts (2010–2015), 1 January, www.marketresearch.com/MarketsandMarkets-v3719/Global-Video-Surveillance-Applications-Management-6083117/.

  • Mehran, R., Moore, E.R. and Shah, M. (2010) A streakline representation of flow in crowded scenes. In: European Conference on Computer Vision (ECCV), Berlin: Springer, 5–11 September. Heraklion, Greece.

  • Moussaid, M., Perozo, N., Garnier, S., Helbing, D. and Theraulaz, G. (2010) The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PLoS ONE 5 (4): e10047.

  • Norris, C. and Armstrong, G. (1999) The Maximum Surveillance Society: The Rise of CCTV. Oxford, UK: Berg.

    Google Scholar 

  • Orrite, C., Martinez, F., Herrero, E., Ragheb, H. and Velastin, S. (2008) Independent viewpoint silhouette-based human action modelling and recognition. In: Proceedings of the 1st International Workshop on Machine Learning for Vision-based Motion Analysis in Conjunction with the European Conference on Computer Vision (ECCV), 17 October. Marseille, France.

  • Park, J.-S. and Lee, S.-W. (2008) An example-based face hallucination method for single-frame, low-resolution facial images. IEEE Transactions on Image Processing 17 (10): 1806–1816.

    Article  Google Scholar 

  • Patino, L., Bremond, F., Evans, M., Shahrokni, A. and Ferryman, J. (2010) Video activity and reporting with incremental unsupervised learning. In: Proceedings Seventh IEEE Advanced Video and Signal-Based Surveillance (AVSS), Sophia Antipolis, France. IEEE Computer Society: Online doi: 10.1109/AVSS.2010.74, pp. 511–518.

  • PETS. (2006) Proceedings of the IEEE 2006 Ninth International Workshop on Performance Evaluation of Tracking and Surveillance. 18 June. http://www.cvg.rdg.ac.uk/PETS2006/PETS2006_PROCEEDINGS.pdf.

  • PETS. (2007) Proceedings of the IEEE 2007 Tenth International Workshop on Performance Evaluation of Tracking and Surveillance. 14 October. http://pets2007.net/PETS2007_PROCEEDINGS.pdf.

  • PETS. (2009) Proceedings of the IEEE 2009 Twelfth International Workshop on Performance Evaluation of Tracking and Surveillance. 25 June. http://www.cvg.rdg.ac.uk/PETS2009/PETS09_PROCEEDINGS.pdf.

  • Pham, Q.-C., Gond, L., Begard, J., Allezard, N. and Sayd, P. (2007) Real time posture analysis in a crowd using thermal imaging. In: Proceedings Computer Vision and Pattern Recognition (CVPR’07), Minneapolis. IEEE Computer Society: Online doi: 10.1109/CVPR.2007.383496, pp. 1–8.

  • Ragheb, H., Velastin, S., Remagnino, P. and Ellis, T. (2008) ViHASi: Virtual human action silhouette data for the evaluation of silhouette-based action recognition methods. In: Proceedings of the ACM International Workshop on Vision Networks for Behaviour Analysis in conjunction with the ACM Multimedia (MM), 31 October. Vancouver, Canada. Piscataway, NJ: IEE.

  • Reeves, B. and Nass, C. (2002) The Media Equation, 2nd edn. Stanford, CA: CSLI.

    Google Scholar 

  • Rowlands, M. (2011) Statewatch analysis – Six months on: An update on the UK coalition government's commitment to civil liberties, 21 January, www.statewatch.org/analyses/no-118-uk-civil-liberties-six-months-on.pdf.

  • Senior, A. et al (2005) Enabling video privacy through computer vision. IEEE Security and Privacy 3 (3): 50–57.

    Article  Google Scholar 

  • Sochman, J. and Hogg, D.C. (2011) Who knows who – Inverting the social force model for finding groups. In: Proceedings IEEE International Workshop on Socially Intelligent Surveillance and Monitoring (SISM), Barcelona, Spain. IEEE Computer Society: Online, doi: 10.1109/1CCVW.2011.6130338, pp. 830–837.

  • Spindler, T., Wartmann, C., Hovestadt, L., Roth, D., Van Gool, L. and Steffen, A. (2008) Privacy in video surveilled spaces. Computer Security 16 (2): 199–222.

    Article  Google Scholar 

  • The Economist. (2012) (28 April). Video surveillance: I spy with my big eye, www.economist.com/node/21553408.

  • Tickner, A.H. and Poulton, E.C. (1973) Monitoring up to 16 synthetic television pictures showing a great deal of movement. Ergonomics 16 (4): 381–401.

    Article  Google Scholar 

  • Tweed, D. and Ferryman, J. (2008) Enhancing change detection in low-quality surveillance video with markov random fields. In: Proceedings of the 1st ACM International Workshop on Vision Networks for Behaviour Analysis (VNBA’08) (in conjunction with the ACM Multimedia Conference). 31 October. Vancouver, Canada. New York: ACM.

  • Valera, M., Velastin, S., Ellis, A-L. and Ferryman, J. (2011) Communication mechanisms and middleware for distributed video surveillance. IEEE Transactions on Circuits and Systems for Video Technology 21 (12): 1795–1809.

    Article  Google Scholar 

  • Welsh, B.P. and Farrington, D.P. (2009) Public area CCTV and crime prevention: An updated systematic review and meta-analysis. Justice Quarterly 26 (4): 716–745.

    Article  Google Scholar 

  • Yilmaz, A., Javed, O. and Shah, M. (2006) Object tracking: A survey. ACM Computing Surveys 38 (4): 1–45.

    Article  Google Scholar 

  • Zaidenberg, S., Boulay, B. and Bremond, F. (2012) A generic framework for video understanding applied to group behaviour recognition. In: Proceedings of the IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2012), Beijing, China. IEEE Computer Society: Online, doi: 10.1109/AVSS.2012.1, pp. 126–142.

  • Zhan, B., Monekosso, D.N., Remagnino, P., Velastin, S.A. and Xu, L.Q. (2008) Crowd analysis: A survey. Machine Vision and Applications 19 (5–6): 345–357.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the UK's EPSRC (EP/G069808/1) and Japan's JSPS (Kakenhi (B) 24330127). This work was supported by the European Union project ARENA (FP7-SEC-2010-1: 261658). However, this paper does not necessarily represent the opinion of the European Community, and the European Community is not responsible for any use which may be made of its contents.

Author information

Authors and Affiliations

  1. Centre for Business Information Ethics, Meiji University, 1-1 Kanda Surugadai, Chiyoda-Ku, Tokyo-To, 101-8301, Japan

    Andrew A Adams

  2. Computational Vision Group, School of Systems Engineering, University of Reading, Reading, RG6 6AY, UK

    James M Ferryman

Authors
  1. Andrew A Adams
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James M Ferryman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrew A Adams.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Adams, A., Ferryman, J. The future of video analytics for surveillance and its ethical implications. Secur J 28, 272–289 (2015). https://doi.org/10.1057/sj.2012.48

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/sj.2012.48

Keywords

Search

Navigation