Geração automática de mapas de dificuldade para datasets

• Ano de defesa: 2022
• Autor(a) principal: Custódio Júnior, Elton
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Cornelio Procopio; Brasil; Programa de Pós-Graduação em Informática; UTFPR
• Palavras-chave em Português: Detectores; Algoritmos; Vídeo digital; Detectors; Algorithms; Digital video; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA; Ciência da Computação
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/30197

Difficulty maps are structures that store estimated difficulty levels for a change detection algorithm to classify each pixel in a video's frames correctly. Such maps are essential to obtain an objective measure, considering the "difficulty level" of the video. Generating a video difficulty map requires using ground truths. Creating a ground truth is considered a laborious process as it involves assigning, often manually, labels to all pixels of all video frames. In this research, a method is presented, which consists of a trained neural network capable of estimating the difficulty level of a video without using a ground truth. We expected that researchers use the results of this work to generate new videos datasets to evaluate change detection algorithms.