2017

Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification

Barbosa, Igor Barros, Cristani, Marco, Caputo, Barbara et al.

Understand

Re-identification is generally carried out by encoding the appearance of a subject in terms of outfit, suggesting scenarios where people do not change their attire.

In this paper we overcome this restriction, by proposing a framework based on a deep convolutional neural network, SOMAnet, that additionally models other discriminative aspects, namely, structural attributes of the human figure (e.g.
height, obesity, gender).
Our method is unique in many respects.

Built on

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