Movimentos sacádicos virtuais baseados em VG-RAM na detecção automática de placas de trânsito

• Ano de defesa: 2013
• Autor(a) principal: Fontana, Cayo Magno da Cruz
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Mestrado em Informática; Centro Tecnológico; UFES; Programa de Pós-Graduação em Informática
• Palavras-chave em Português: Redes neurais (Computação); Trânsito - Sinais e sinalização; Olhos - Movimentos; Reconhecimento de padrões; Memória de acesso aleatório; Simetria; Ciência da Computação; 004
• Link de acesso: http://repositorio.ufes.br/handle/10/6368

The task of detecting and recognizing road signs in real environments have been widely researched in recent years. Recently, the number of vehicles on urban roads has grown exponentially. Big problems in these pathways have emerged a result of this growth. Statistics of the United Nations (UN), points traffic accidents as a leading cause of death in the world. With the aim of assisting drivers in the task of detecting and recognizing road signs to alert them about possible changes in the way, or even act to control the car, we present in this dissertation a biologically inspired approach to detect traffic signs based on a Virtual Generalizing Random Access Memory Weightless Neural Networks - VG-RAM WNN. VG-RAM WNN are effective machine learning tools that offer simple implementation and fast training and test. Our VG-RAM WNN architecture models the saccadic eye movement system and the transformations suffered by the images captured by the eyes from the retina to the superior colliculus in the mammalian brain. We evaluated the performance of our VG-RAM WNN system on traffic sign detection using the German Traffic Sign Detection Benchmark (GTSDB). Using only 12 traffic sign images for training, our system was ranked in the 16th position, in the total 53 methods submitted among 18 teams, for the prohibitory category in the German Traffic Sign Detection Competition, part of the IJCNN 2013. Our experimental results showed that our approach is capable of reliably and efficiently detect a large variety of traffic sign categories using a few training samples.