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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