A Neuro‐Fuzzy Crime Prediction Model Based on Video Analysis. Issue 5 (1st September 2018)

• Record Type: Journal Article
• Authors: Zou, Beiji; Nurudeen, Mohammed; Zhu, Chengzhang; Zhang, Ziqian; Zhao, Rongchang; Wang, Lei
• Is Part Of: Chinese journal of electronics. Volume 27:Issue 5(2018)
• Page Start: 968
• Page End: 975
• Publication Date: 2018-09-01
• Subjects: Hybrid inte lligent syste ms (HIS) -- Subtractive clustering -- Video event detection -- Crime prediction; feature extraction -- fuzzy neural nets -- fuzzy reasoning -- object detection -- social sciences computing -- video signal processing -- video surveillance; neuro‐fuzzy crime prediction model -- video analysis -- hybrid neuro‐fuzzy model -- simulated wide area surveillance network -- crime modeling phases -- concept based approach -- video event detection -- extracted indicators -- fuzzy inference system -- UCR‐Videoweb dataset -- violent scene detection 2014 dataset -- crime indicator event detection; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2018.02.019
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : 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.