Analisando a viabilidade de deep learning para reconhecimento de a??es em datasets pequenos

• Ano de defesa: 2018
• Autor(a) principal: Santos Junior, Juarez Monteiro dos
• Orientador(a): Barros, Rodrigo Coelho
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Pontif?cia Universidade Cat?lica do Rio Grande do Sul
• Programa de Pós-Graduação: Programa de P?s-Gradua??o em Ci?ncia da Computa??o
• Departamento: Escola Polit?cnica
• País: Brasil
• Palavras-chave em Português: Aprendizado de M?quina; Redes Neurais; Redes Neurais Convolucionais; Reconhecimento de A??es
• Palavras-chave em Inglês: Machine Learning; Neural Networks; Convolutional Neural Networks; Action Recognition
• Área do conhecimento CNPq: CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO
• Link de acesso: http://tede2.pucrs.br/tede2/handle/tede/8036

Action recognition is the computer vision task of identifying which action is happening in a given sequence of frames. Traditional approaches rely on handcrafted features and domain specific algorithms, often resulting in limited accuracy. The substantial advances in deep learning and the availability of larger datasets have allowed techniques that yield better performance without domain-specific knowledge to recognize actions being performed based on the raw information from video sequences. However, deep learning algorithms usually require very large labeled datasets for training, and due to their increased capacity their often overfit small data, hence providing lower generalization power. This work aims to explore deep learning in the context of small-sized action recognition datasets. Our goal is to achieve significant performance even in cases in which labeled data is not abundant. In order to do so, we investigate distinct network architectures, data pre-processing, and fusion methods, providing guidelines and good practices for using deep learning in small-sized datasets.