Crime Scene Representation (2D, 3D, Stereoscopic Projection) and Classification
Ricardo O. Abu Hana (Catholic University of Paraná, Brazil)
Cinthia O.A. Freitas (Catholic University of Paraná, Brazil)
Luiz S. Oliveira (Catholic University of Paraná, Brazil)
Flávio Bortolozzi (OPET College, Brazil)
Abstract: 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.
Keywords: SVM, classification, crime scenes, features, neural networks