RT Journal Article
T1 Fisher Motion Descriptor for Multiview Gait Recognition.
A1 Castro, Francisco M.
A1 Marín Jiménez, Manuel Jesús
A1 Guil-Mata, Nicolás
A1 Muñoz-Salinas, Rafael
K1 Arquitectura de ordenadores
AB This paper aims to identify individuals by analyzing their gait using motion descriptors based on densely sampled short-term trajectories, instead of traditional binary silhouettes. The approach leverages advanced people detectors to create detailed spatial configurations around the person, capturing rich gait motion. Local motion features, combined using Fisher Vector encoding, result in a high-level gait descriptor called Pyramidal Fisher Motion. The method is validated on multiple datasets (CASIA, TUM GAID, CMU MoBo, and AVA Multiview Gait), achieving state-of-the-art results in recognizing individuals across various conditions such as different viewpoints, clothing, speeds, and walking paths.
PB World Scientific
YR 2017
FD 2017
LK https://hdl.handle.net/10630/32712
UL https://hdl.handle.net/10630/32712
LA eng
NO International Journal of Pattern Recognition and Artificial Intelligence, Volumen 31, Número 01, Páginas 1756002
NO Política de acceso abierto tomada de: https://v2.sherpa.ac.uk/id/publication/9703
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026