JUCS - Journal of Universal Computer Science 14(18): 2953-2966, doi: 10.3217/jucs-014-18-2953
Crime Scene Representation (2D, 3D, Stereoscopic Projection) and Classification
Ricardo O. Abu Hana‡, Cinthia O.A. Freitas‡, Luiz S. Oliveira§, Flávio Bortolozzi|‡ Catholic University of Paraná, Curitiba, Brazil§ Pontifícia Universidade Católica do Paraná, Curitiba, Brazil| OPET College, Brazil
Corresponding author: Ricardo O. Abu Hana ( ricardohana@ppgia.pucpr.br ) © Ricardo O. Abu Hana, Cinthia Freitas, Luiz Oliveira, Flávio Bortolozzi. This article is freely available under the J.UCS Open Content License. Citation:
Hana ROA, Freitas CO.A, Oliveira LS, Bortolozzi F (2008) Crime Scene Representation (2D, 3D, Stereoscopic Projection) and Classification. JUCS - Journal of Universal Computer Science 14(18): 2953-2966. https://doi.org/10.3217/jucs-014-18-2953 |  |
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
classification, neural networks, SVM, features, crime scenes