A Detecção de Anomalias em Vídeos (DAV) é assunto recorrente em visãocomputacional. Neste trabalho, DAV refere-se ao problema de encontrar padrões emvídeos que se afastem de um conceito normal esperado. Assim, é proposto o estudodos Autocodificadores Convolucionais (ACC) como meio de aprendizado decaracterísticas aplicado ao problema de DAV. A metodologia proposta segue o viéshipotético-dedutivo, onde a principal hipótese de trabalho é que ACCs são capazesde aprender características relevantes na classificação de eventos consideradosanormais. Como resultados, é apresentado revisão da literatura sobre DAV e ACCs,além de experimento computacional utilizando a base dados UCSD-PED2.

The quality of images obtained from video surveillance systems is a decisive aspect when performing investigations at the Forensic Science. Features such as scars, tattoos, and skin marks are great examples of details that allow to consolidate an investigation at certain scenarios in which there is the necessity to identify individuals captured in a video or image footage. However, the low quality of images could affect the results of the investigations. In this sense, this work proposes the study of a computational model to address the problem of increasing the resolution of Low-Resolution (LR) images, also known as the problem of super-resolution of images. The main idea is to train a Generative Adversarial Network (GAN) so that it can be able to enhance low-quality images. The hypothesis is that a variant model of a GAN, named Super-Resolution Generative Adversarial Network (SRGAN), is capable to produce High-Resolution (HR) images from LR ones. The proposed methodology is based on experimental research with the aid of the hypothetical deductive method, where two well-recognised state of art methods were used, which proposes the use of convolutional neural networks and deep learning. For the model validation, were conducted four different experiments: two to avail the capacity of the GAN to produce images with enhanced resolution and two other experiments to evaluate the quality of the results produced by the SRGAN. The quantitative results of our experiments are promising, with performances that are similar to those obtained by state-of-the-art approaches. Moreover, the qualitative results based on performing a visual analysis of the images produced by our approach suggest a interesting performance in terms of visual quality.