Crime Prediction Based on Person-Weapons Relation using Deep Learning Techniques

Abstract

Crime Scene detection or predicting the chances of happening the crime without any involvement of human intervention is always the crucial task in the field of artificial intelligence. In this paper crime forecasting based on the weapon detection and tracking with the person can help investigator to understand the sequence of action took place during the crime. The images are manually annotated, which is a process where an expert goes through each images and mark the position and class of object within the image. Object detection and classification algorithms provides the necessary ground to verify data for the algorithms. The models like SSD, YOLO and Faster RCNN are used for weapons detection and mediapipe library is used to generate the human body datapoints and calculate the relation between weapons with the human. The maximum accuracy of Faster RCNN with mediapipe library is 97%.