2016

End-to-End Comparative Attention Networks for Person Re-identification

Liu, Hao, Feng, Jiashi, Qi, Meibin et al.

Understand

Person re-identification across disjoint camera views has been widely applied in video surveillance yet it is still a challenging problem.

One of the major challenges lies in the lack of spatial and temporal cues, which makes it difficult to deal with large variations of lighting conditions, viewing angles, body poses and occlusions.
Recently, several deep learning based person re-identification approaches have been proposed and achieved remarkable performance.
However, most of those approaches extract discriminative features from the whole frame at one glimpse without differentiating various parts of the persons to identify.

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