Automatic learning of gait signatures for people identification
Autor
Castro, F.M.
Marín-Jiménez, M.J.
Guil, N.
Pérez de la Blanca, N.
Fecha
2017-12-05Materia
Gait recognitionPeople identification
Convolutional neural networks
METS:
Mostrar el registro METSPREMIS:
Mostrar el registro PREMISMetadatos
Mostrar el registro completo del ítemResumen
This work targets people identification in video based
on the way they walk (i.e. gait). While classical methods
typically derive gait signatures from sequences of binary
silhouettes, in this work we explore the use of convolutional
neural networks (CNN) for learning high-level descriptors
from low-level motion features (i.e. optical flow
components). We carry out a thorough experimental evaluation
of the proposed CNN architecture on the challenging
TUM-GAID dataset. The experimental results indicate that
using spatio-temporal cuboids of optical flow as input data
for CNN allows to obtain state-of-the-art results on the gait
task with an image resolution eight times lower than the
previously reported results (i.e. 80 60 pixels).