References
- Antonakaki, P., Kosmopoulos, D., & Perantonis, S. J. (2009). Detecting abnormal human behaviour using multiple cameras. Signal Processing, 89(9), 1723–1738. doi: 10.1016/j.sigpro.2009.03.016
- Black, J., Velastin, S., & Boghossian, B. (2005, September 15–16). A real time surveillance system for metropolitan railways. Paper presented at the IEEE international conference on advanced video and signal-based surveillance, Como, Italy.
- Calder, R. (2005, August 24). War on terror; tunnel ‘vision’: Lockheed to start immediately putting 1,000 cams and 3,000 motion sensors in every subway station. The New York Post.
- Call, C., Reitz, E. D., & Tillotson, D. A. (2007). Automatic encoding of a complex system architecture in a pattern recognition classifier (US Patent 7,313,267). Bethesda, MD: Lockheed Martin Corporation.
- Candamo, J., Shreve, M., Goldgof, D., Sapper, D., & Kasturi, R. (2010). Understanding transit scenes: A survey on human behavior-recognition algorithms. IEEE Transactions on Intelligent Transportation Systems, 11(1), 206–224. doi: 10.1109/TITS.2009.2030963
- Chakrabarti, S., & Strauss, A. (2002). Carnival booth: An algorithm for defeating the computer-assisted passenger screening system. Course paper, MIT 6.806: Law and ethics on the electronics frontier. Retrieved from http://groups.csail.mit.edu/mac/classes/6.805/student-papers/spring02-papers/caps.htm
- Chan, S. (2005, August 24). MTA to keep an electronic eye on the subway. The New York Times.
- Coaffee, J. (2003). Terrorism, risk, and the city: The making of a contemporary urban landscape. Aldershot: Ashgate.
- Cong, D. N. T., Khoudour, L., Achard, C., & Bruyelle, J.-L. (2011). Intelligent distributed surveillance system for people reidentification in a transportation environment. Journal of Intelligent Transportation Systems, 15(3), 133–146. doi: 10.1080/15472450.2011.594672
- Davies, E. R. (2012). Computer machine vision: Theory, algorithms, practicalities (4th ed.). London: Academic Press.
- Dee, H. M., & Velastin, S. A. (2008). How close are we to solving the problem of automated visual surveillance? A review of real-world surveillance, scientific progress and evaluative mechanisms. Machine Vision and Applications, 19(5–6), 329–343. doi: 10.1007/s00138-007-0077-z
- Dubbeld, L. (2004). Limits on surveillance: Fractures, fragilities and failures in the operation of camera surveillance. Journal of Information, Communication and Ethics in Society, 2(1), 9–19. doi: 10.1108/14779960480000239
- Gates, K. (2011). Our biometric future: Facial recognition technology and the culture of surveillance. New York: NYU Press.
- Graham, S. (2006). Cities and the ‘war on terror’. International Journal of Urban and Regional Research, 30(2), 255–276. doi: 10.1111/j.1468-2427.2006.00665.x
- Ho, T. K., Matthews, K., O’Gorman, L., & Steck, H. (2012). Public space behavior modelling with video and sensor analytics. Bell Labs Technical Journal, 16(4), 203–217. doi: 10.1002/bltj.20542
- Hoiem, D., Efros, A. A., & Hebert, M. (2011). Recovering occlusion boundaries from an image. International Journal of Computer Vision, 91(3), 328–346. doi: 10.1007/s11263-010-0400-4
- Introna, L. D., & Wood, D. (2004). Picturing algorithmic surveillance: The politics of facial recognition systems. Surveillance & Society, 2(2–3), 177–198.
- Joh, E. E. (2016). The new surveillance discretion: Automated suspicion, big data, and policing. Harvard Law & Policy Review, 10, 15–42.
- Klinenberg, E., & Lakoff, A. (2010). Of risk and pork: Urban security and the politics of objectivity. Theory and Society, 39, 503–525. doi: 10.1007/s11186-010-9123-3
- Marcuse, P. (2006). Security or safety in cities? The threat of terrorism after 9/11. International Journal of Urban and Regional Research, 30(4), 919–929. doi: 10.1111/j.1468-2427.2006.00700.x
- Mason, D., Button, G., Lankshear, G., Coats, S., & Sharrock, W. (2002). On the poverty of apriorism: Technology, surveillance in the workplace and employee responses. Information, Communication & Society, 5(4), 555–572. doi: 10.1080/13691180208538806
- Mollers, N., & Halterlein, J. (2013). Privacy issues in public discourse: The case of ‘smart’ CCTV in Germany. Innovation: The European Journal of Social Science Research, 26(1–2), 57–70.
- Molotch, H. (2012). Against security: How we go wrong at airports, subways and other sites of ambiguous danger. Princeton, NJ: Princeton University Press.
- Monachino, C. A., & Paradis, R. D. (2007). Scene analysis surveillance system (US Patent 7,310,442). Bethesda, MD: Lockheed Martin Corporation.
- Nelson, L. S. (2011). America identified: Biometric technology and society. Cambridge, MA: MIT Press.
- Newman, O. (1972). Defensible space: Crime prevention through urban design. New York, NY: Macmillan.
- Norris, C., & Armstrong, G. (1999). The maximum surveillance society: The rise of CCTV. Oxford: Berg.
- Prates, R. C., Camara-Chavez, G., Schwartz, W. R., & Menotti, D. (2014). An adaptive vehicle license plate detection at higher matching degree. In E. Bayro-Corrochono & E. Hancock (Eds.), Progress in pattern recognition, image analysis, computer vision, and applications (pp. 454–461). London: Springer.
- Raab, C. D. (2002). Surveillance: The need for research evidence. Information, Communication & Society, 4(5), 551–554. doi: 10.1080/13691180208538805
- Regan, P. M., Monahan, T., & Craven, K. (2015). Constructing the suspicious: Data production, circulation, and interpretation by DHS fusion centers. Administration & Society, 47(6), 740–762. doi: 10.1177/0095399713513141
- Sallaz, J. (2009). The labor of luck: Casino capitalism in the United States and South Africa. Berkeley: University of California Press.
- Sjarif, N. N. A., Shamsuddin, S. M., & Hashim, S. Z. (2012). Detection of abnormal behaviors in crowd scenes: A review. International Journal of Advances in Soft Computing & Its Application, 4(1), 1–33.
- Staples, W. G. (2005). The culture of surveillance revisited: ‘Total information awareness’ and the new privacy landscape. Social Thought and Research, 126, 123–135.
- White, D. J., Svellingen, C., & Strachan, N. (2006). Automated measurement of species and length of fish by computer vision. Fisheries Research, 80, 203–210. doi: 10.1016/j.fishres.2006.04.009