Deep Neural Networks and Convolution Neural Network for Computer Vision based Crime Similarity Pattern Analysis

In today’s society, preventing and mitigating crime is crucial for maintaining public safety, promoting community well-being, and ensuring the overall security of a society. Each day, criminals perpetrate thousands of crimes. In the field of law enforcement and public safety, crime pattern similarity analysis is a crucial task that aims to unearth hidden linkages and repeating behaviors inside criminal episodes. Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) are two concepts of advanced neural network designs that can be integrated to improve understanding and prediction of crime patterns similarity analysis. This study explores the use of CNNs and DNNs in crime pattern similarity analysis, offering a novel strategy to help law enforcement organizations understand the complex relationships in crime-related data. This research aims to improve crime prevention, investigation, and public safety by exposing hidden patterns, foreseeing upcoming trends, and enabling data-driven decision-making. It is feasible to improve the model’s convergence, stability, and capacity to learn efficiently from various and complicated surveillance image data by using RMSprop as an optimizer when training CNN and DNN models for crime similarity pattern identification in computer vision applications.