Optimizing Person Re-Identification Using Generated Attention Masks

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2021

Autores

Capozzi, L (Autor) Outra; Pinto, JR (Autor) Outra; Jaime S Cardoso (Autor) FEUP; Rebelo, A (Autor) Outra

Ata de Conferência Internacional

Título: CIARP

Páginas: 248-257

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00V-YDZ

DOI: 10.1007/978-3-030-93420-0_24

Abstract (EN): The task of person re-identification has important applications in security and surveillance systems. It is a challenging problem since there can be a lot of differences between pictures belonging to the same person, such as lighting, camera position, variation in poses and occlusions. The use of Deep Learning has contributed greatly towards more effective and accurate systems. Many works use attention mechanisms to force the models to focus on less distinctive areas, in order to improve performance in situations where important information may be missing. This paper proposes a new, more flexible method for calculating these masks, using a U-Net which receives a picture and outputs a mask representing the most distinctive areas of the picture. Results show that the method achieves an accuracy comparable or superior to those in state-of-the-art methods.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9