ZOU Beiji, Nurudeen Mohammed, ZHU Chengzhang, ZHANG Ziqian, ZHAO Rongchang, WANG Lei. A Neuro-Fuzzy Crime Prediction Model Based on Video Analysis[J]. Chinese Journal of Electronics, 2018, 27(5): 968-975. doi: 10.1049/cje.2018.02.019
Citation: ZOU Beiji, Nurudeen Mohammed, ZHU Chengzhang, ZHANG Ziqian, ZHAO Rongchang, WANG Lei. A Neuro-Fuzzy Crime Prediction Model Based on Video Analysis[J]. Chinese Journal of Electronics, 2018, 27(5): 968-975. doi: 10.1049/cje.2018.02.019

A Neuro-Fuzzy Crime Prediction Model Based on Video Analysis

doi: 10.1049/cje.2018.02.019
Funds:  This work is supported by the National Natural Science Foundation of China (No.61573380, No.61702559) and the Fundamental Research Funds for the Central Universities of Central South University (No.2017zzts715, No.2017zzts723).
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  • Corresponding author: ZHU Chengzhang (corresponding author) received her M.E. degree in computer science and education from Huazhong University of Science and Technology, Wuhan, in 2006 and her Ph.D. degree in control science and engineering from School of Information Science and Engineering, Central South University, Changsha, in 2016. Currently, she is a faculty of the College of Literature and Journalism, Central South University, Changsha. Her research interests include medical image processing, computer vision and pattern recognition. (Email:anandawork@126.com)
  • Received Date: 2016-12-29
  • Rev Recd Date: 2017-11-15
  • Publish Date: 2018-09-10
  • A hybrid neuro-fuzzy model for predicting crime in a wide area such as a town or district in presented. The model is built using what we describe as crime indicator events extracted from simulated wide area surveillance network. The framework principally involves two phases, namely video analysis and crime modeling phases. In video analysis a concept based approach for video event detection is used to detect crime indicator events. Based on the extracted indicators with other related variables, a fuzzy inference system capable of learning is constructed in the second phase. The model is constructed using Violent scene detection (VSD) 2014 dataset and testing is done using UCR-Videoweb dataset. The experimental results show that the proposed method is quite demonstrative and promising.
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  • L.E. Ċohen and M. Felson, “Social change and crime rate trends: A routine activity approach”, American Sociological Review, Vol.44, No.4, pp.588-608, 1979.
    H. Li, S. Liu and H. Wang, “Separation of objects with unclear edges from the nonuniform background”, Chinese Journal of Electronics, Vol.20, No.1, pp.85-90, 2011.
    M.A. Tayebi, U. Glasser and P.L. Brantingham, “Learning where to inspect: Location learning for crime prediction”, IEEE International Conference on Intelligence and Security Informatics, Baltimore, MD, USA, pp.25-30, 2015.
    X. Song, L. Sun, J. Lei, et al., “Event-based large scale surveillance video summarization”, Neurocomputing, Vol.187, Part.C, pp.66-74, 2016.
    I. Hamid and S. Mubarak, “Recognizing complex events using large margin joint low-level event model”, Izadinia, Hamid and Shah, Mubarak, Vol.7575, No.1, pp.430-444, 2012.
    E.B. Zare, A. Dehghan, M. Piccardi, et al., “Complex event recognition by latent temporal models of concepts”, IEEE International Conference on Image Processing, CNIT La Defense, Paris, France, pp.2373-2377, 2014.
    M.A. Shayan, R.Z. Amir and S. Mubarak, “Video classification using semantic concept co-occurrences”, The IEEE Conference on Computer Vision and Pattern Recognition, Columbus, Ohio, USA, pp.2529-2536, 2014.
    B. Zou, N. Mohammed, C. Zhu, et al., “Crime hotspot detection and monitoring using video based event modeling and mapping techniques”, International Journal of Computational Intelligence Systems, Vol.10, No.1, pp.962, 2017.
    W. Li, Y. Bi, X. Zhu, et al., “Hybrid swarm intelligent parallel algorithm research based on multi-core clusters”, Microprocessors and Microsystems, Vol.47, Part.A, pp.151-160, 2016.
    J.R. Jang, “ANFIS: Adaptive-network-based fuzzy inference systems”, IEEE Transactions on Systems, Man, and Cybernetics, Vol.23, No.3, pp.665-685, 1993.
    H.R. Bherenji and P. Khedkar, “Learning and tuning fuzzy logic controllers through reinforcements”, IEEE Transactions on Neural Networks, Vol.3, No.5, pp.724-740, 1992.
    D. Nauck and R. Kruse, “Neuro-fuzzy systems for function approximation”, Journal Fuzzy Sets and Systems-Special Issue on Analytical and Structural Considerations in Fuzzy Modeling, Vol.101, No.2, pp.261-271, 1999.
    S. Chiu, “Fuzzy model identification based on cluster estimation”, Journal of Intelligent and Fuzzy Systems, Vol.2, No.3, pp.267-268, 1994.
    R. Yager and D. Filev, “Generation of fuzzy rules by mountain clustering”, Journal of Intelligent and Fuzzy, Vol.2, No.3, pp.207-219, 1994.
    Y. Yuan, X. Song, H. Sahbi, et al., “Spatio-temporal interest points chain (STIPC) for activity recognition”, Asian Conference on Pattern Recognition, pp.22-26, 2011.
    L.A. Umit and Y. Adnan, “An improved BOW approach using fuzzy feature encoding and visual-word weighting”, IEEE International Conference on Fuzzy Systems, Istanbul, Turkey, pp.1-5, 2015.
    C. Ding, A. Kamal, G. Denina, et al., “Videoweb activities dataset, ICPR contest on semantic description of human activities (SDHA), http://cvrc.ece.utexas.edu/SDHA2010/ Wide Area Activity.html, 2016-12-28.
    M. Sjoberg, B. Ionescu, V.L. Quang, et al., “The MediaEval 2014 affect task: Violent scenes detection”, Working Notes Proceedings of the MediaEval 2014 Workshop, Barcelona, Catalunya, Spain, 2014.
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