  Video-based person re-identification (Re-ID) which aims to associate people
across non-overlapping cameras using surveillance video is a challenging task.
Pedestrian attribute, such as gender, age and clothing characteristics contains
rich and supplementary information but is less explored in video person Re-ID.
In this work, we propose a novel network architecture named Attribute Salience
Assisted Network (ASA-Net) for attribute-assisted video person Re-ID, which
achieved considerable improvement to existing works by two methods.First, to
learn a better separation of the target from background, we propose to learn
the visual attention from middle-level attribute instead of high-level
identities. The proposed Attribute Salient Region Enhance (ASRE) module can
attend more accurately on the body of pedestrian. Second, we found that many
identity-irrelevant but object or subject-relevant factors like the view angle
and movement of the target pedestrian can greatly influence the two dimensional
appearance of a pedestrian. This problem can be mitigated by investigating both
identity-relevant and identity-irrelevant attributes via a novel triplet loss
which is referred as the Pose~\&~Motion-Invariant (PMI) triplet loss.
