  Person Re-Identification (person re-id) is a crucial task as its applications
in visual surveillance and human-computer interaction. In this work, we present
a novel joint Spatial and Temporal Attention Pooling Network (ASTPN) for
video-based person re-identification, which enables the feature extractor to be
aware of the current input video sequences, in a way that interdependency from
the matching items can directly influence the computation of each other's
representation. Specifically, the spatial pooling layer is able to select
regions from each frame, while the attention temporal pooling performed can
select informative frames over the sequence, both pooling guided by the
information from distance matching. Experiments are conduced on the iLIDS-VID,
PRID-2011 and MARS datasets and the results demonstrate that this approach
outperforms existing state-of-art methods. We also analyze how the joint
pooling in both dimensions can boost the person re-id performance more
effectively than using either of them separately.
