Publicación:
An enhanced triplet CNN based on body parts for person re-identificacion
An enhanced triplet CNN based on body parts for person re-identificacion
No hay miniatura disponible
Fecha
2018
Autores
Espinoza J.D.
Chavez G.C.
Torres G.H.
Título de la revista
Revista ISSN
Título del volumen
Editor
IEEE Computer Society
Proyectos de investigación
Unidades organizativas
Número de la revista
Abstracto
Person re-identificacion consists of reidentificating person through a set of images that is taken by different camera views. Despite recent advances in this field, this problem still remains a challenge due to partial occlusions, changes in illumination, variation in human body poses. In this paper, we present an enhanced Triplet CNN based on body-parts for person re-identification (AETCNN). We design a new model able to learn local body-part features and integrate them to produce the final feature representation of each input person. In addition, to avoid over-fitting due to the small size of the dataset, we propose an improvement in triplet assignment to speed up the convergence and improve performance. Experiments show that our approach achieves very promising results in (CUHK01) dataset and we advance state of the art, improving most of the results of the state of the art with a simpler architecture, achieving 76.50% in rank 1.
Descripción
This work was supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of the National Council for Science,Technology and Technological Innovation (CONCYTEC-PERU).
Palabras clave
State of the art,
Computers,
Camera view,
Feature representation,
Human bodies,
Improve performance,
Overfitting,
Partial occlusions,
Person re identifications,
Computer science