Crime Scene Representation (2D, 3D, Stereoscopic Projection) and Classification
In this paper we provide a study about crime scenes and its features used in criminal investigations. We argue that the crime scene provides a large set of features that can be used to corroborate the conclusions emitted by the experts. We also propose a set of features to classify the violent crime considering two classes: attack from inside or outside of the scene. The classification stage is based on conventional MLP (Multiple-Layer Perceptron) Neural Network and SVM (Support Vector Machine). The experimental results reveal an error rate of 30.3% (MLP), 22.8% (SVM-linear), and 19.4% (SVM-polynomial) using a database composed of 400 crime scenes. This paper presents an experiment based on a stereoscopic projection that allows to experts analyze and take decisions about the crime scene and its dynamic.
crime scenes classification features neural networks svm