Crime Hotspot Detection and Monitoring Using Video Based Event Modeling and Mapping Techniques

Zou Beiji, Nurudeen Mohammed, Zhu Chengzhang, Zhao Rongchang
2017 International Journal of Computational Intelligence Systems  
This paper presents a new approach to crime hotspot detection and monitoring. The approach consists of three phases' namely: video analysis, crime prediction and crime mapping. In video analysis, crime indicator events are modelled using statistical distribution of semantic concepts. In crime prediction, a neuro-fuzzy method is used to model indicator events. In crime mapping, kernel density estimation is used to detect crime hotspots. This approach is tested in a simulated platform using violent scene detection (VSD) 2014 dataset.
doi:10.2991/ijcis.2017.10.1.64 fatcat:vy44mlqpvrem7nauakjfhnklpm