Detecting Motion Patterns in Dense Flow Fields: Euclidean Versus Polar Space

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2015

Autores

Pinto, A (Autor) FEUP; Paulo Gomes da Costa (Autor) FEUP; António Paulo Moreira (Autor) FEUP

Ata de Conferência Internacional

Título: PROGRESS IN ARTIFICIAL INTELLIGENCE

Páginas: 487-492

17th Portuguese Conference on Artificial Intelligence (EPIA)

Univ Coimbra, Coimbra, PORTUGAL, SEP 08-11, 2015

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00G-TWG

DOI: 10.1007/978-3-319-23485-4_48

Abstract (EN): This research studies motion segmentation based on dense optical flow fields for mobile robotic applications. The optical flow is usually represented in the Euclidean space however, finding the most suitable motion space is a relevant problem because techniques for motion analysis have distinct performances. Factors like the processing-time and the quality of the segmentation provide a quantitative evaluation of the clustering process. Therefore, this paper defines a methodology that evaluates and compares the advantage of clustering dense flow fields using different feature spaces, for instance, Euclidean and Polar space. The methodology resorts to conventional clustering techniques, Expectation-Maximization and K-means, as baseline methods. The experiments conducted during this paper proved that the K-means clustering is suitable for analyzing dense flow fields.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6