Publicación:
An enhanced triplet CNN based on body parts for person re-identificacion

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Fecha
2018
Autores
De Zela F.
Guerra Torres, Jorge Andrés
Montañez L.M.
Töfflinger J.A.
Tucto K.
Weingärtner R.
Winnaker A.
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IEEE Computer Society
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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.
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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).
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