Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Learning Method for Real-time Crime Prediction Model Utilizing CCTV

  • Bang, Seung-Hwan (Dept. of Industrial and Management Engineering, Pohang University of Science and Technology) ;
  • Cho, Hyun-Bo (Dept. of Industrial and Management Engineering, Pohang University of Science and Technology)
  • Received : 2016.02.19
  • Accepted : 2016.05.17
  • Published : 2016.05.31
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Abstract

We propose a method to train a model that can predict the probability of a crime being committed. CCTV data by matching criminal events are required to train the crime prediction model. However, collecting CCTV data appropriate for training is difficult. Thus, we collected actual criminal records and converted them to an appropriate format using variables by considering a crime prediction environment and the availability of real-time data collection from CCTV. In addition, we identified new specific crime types according to the characteristics of criminal events and trained and tested the prediction model by applying neural network partial least squares for each crime type. Results show a level of predictive accuracy sufficiently significant to demonstrate the applicability of CCTV to real-time crime prediction.

Keywords

References

  1. C. Shu, A. Hampapur, M. Lu, L. Brown, J. Connell, A. Senior, and Y. Tian, "IBM Smart Surveillance System(S3): A Open and Extensible Framework for Event based Surveillance", IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 318-323, Como, Italy, September 2005.
  2. Predpol, http://www.predpol.com/
  3. J. W. Brahan, K. P. Lam, H. Chan and W. Leung, "AICAMS: Artificial Intelligence Crime Analysis and Management System", Knowledge-Based Systems, Vol. 11, No. 5, pp. 355-361, November, 1998. https://doi.org/10.1016/S0950-7051(98)00064-1
  4. Y. S. Chung, J. M. Kim and K. R. Park, "A Study of Improved Ways of the Predicted Probability to Criminal Types", Journal of The Korea Society of Computer and Information, Vol 17, No. 4, pp 163-172, April, 2012. https://doi.org/10.9708/jksci.2012.17.4.163
  5. Y. H. Kim and J. M. Mun, "A Study on the Development of Crime Prediction Program(CPP)", Journal of The Korea Society of Computer and Information, Vol. 11, No. 4, pp. 221-230, December, 2006.
  6. Daejeon Metropolitan City, http://www.daejeon.go.kr/uic/index.do
  7. T. Troscianko, A. Holmes, J. Stillman, M. Mirmehdi, D. Wright and A. Wilson, "What Happens Next? The Predictability of Natural Behaviour Viewed Through CCTV Cameras, Perception, Vol. 33, No. 1, pp. 87-101, January, 2004. https://doi.org/10.1068/p3402
  8. D. Grant and D. Williams, "The Importance of Perceiving Social Contexts When Predicting Crime and Antisocial Behaviour in CCTV Images", Legal and Criminological Psychology, Vol. 16, No. 2, pp. 307-322, September, 2011. https://doi.org/10.1348/135532510X512665
  9. I. Darker, A. Gale, L. Ward and A. Blechko, "Can CCTV reliably detect gun Crime?", IEEE Conference on Security Technology, pp 264-271. October, 2007.
  10. S. H. Bang, T. H. Kim and H. B. Cho, "A Study on the Applicability of Data Mining for Crime Prediction : Focusing on Burglary", Journal of The Korea Society of Computer and Information, Vol. 19, No. 12, December, 2014.
  11. Seoul Statistics, http://stat.seoul.go.kr
  12. L. Cohen, and M. Felson, "Social Change and Crime Rate Trends: A Route Activity Approach," American Sociological Review, Vol. 44, No. 4, pp. 588-608, August, 1979.
  13. P. Brantingham, and P. Brantingham, "Environmental Criminology," Wavelend Press Inc, pp. 27-54, 1991.
  14. S. Y. Ko, K. O. Kim, Y. D. Jung and D. H. Choi, "A Study to Classify Serial Sex Offenders Based on Crime Scene Actions", The Korean Journal of Forensic Psychology, Vol. 1, No. 3, pp. 171-183, November, 2010.
  15. J. R. Quinlan, "Introduction of decision trees", Machine learning, Vol. 1, No. 1, pp. 81-106, March, 1986. https://doi.org/10.1007/BF00116251
  16. J. R. Quinlan, "C4.5: Programs for Machine Learning", Elsevier, 2014.
  17. G. V. Kass, "An Exploratory Technique for Investigating Large Quantities of Categorical Data", Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 29, No. 2, pp. 119-127, 1980.
  18. L. Breiman, J. Friedman, C. Stone and R.A. Olshen, "Classification and Regression Trees", CRC press, 1984
  19. Y. D. Kim, C. H. Jun and H. S. Lee, "A New Classification Method Using Penalized Partial Least Squares", Journal of the Korean Data & Information Science Society, Vol. 22, No. 5, pp. 931-940, October, 2011.
  20. E. Malthouse, A. Tamhane and R. Mah. "Nonlinear Partial Least Squares". Computers and Chemical Engineering, Vol. 21, No. 8, pp. 875-890, April, 1997. https://doi.org/10.1016/S0098-1354(96)00311-0
  21. S. Wold, M. Sjostrom and L. Eriksson, "PLS-regression: a Basic Tool of Chemometrics", Chemometrics and Intelligent Laboratory Systems, Vol. 58, No. 2, pp. 109-130, October, 2001. https://doi.org/10.1016/S0169-7439(01)00155-1
  22. F. Famili, W. M. Shen, R. Weber and E. Simoudis, "Data Pre-processing and Intelligent Data Analysis", International Journal on Intelligent Data Analysis, Vol. 1, No. 1, pp. 1-28, March, 1997. https://doi.org/10.1016/S1088-467X(98)00006-7
  23. S. Zhang, C. Zhang and Q. Yang, "Data Preparation for Data Mining", Applied Artificial Intelligence, Vol. 17, No. 5-6, pp. 375-381, November, 2003. https://doi.org/10.1080/713827180

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