  Existing methods for video-based person re-identification (ReID) mainly learn
the appearance feature of a given pedestrian via a feature extractor and a
feature aggregator.
  However, the appearance models would fail when different pedestrians have
similar appearances.
  Considering that different pedestrians have different walking postures and
body proportions, we propose to learn the discriminative pose feature beyond
the appearance feature for video retrieval.
  Specifically, we implement a two-branch architecture to separately learn the
appearance feature and pose feature, and then concatenate them together for
inference.
  To learn the pose feature, we first detect the pedestrian pose in each frame
through an off-the-shelf pose detector, and construct a temporal graph using
the pose sequence.
  We then exploit a recurrent graph convolutional network (RGCN) to learn the
node embeddings of the temporal pose graph, which devises a global information
propagation mechanism to simultaneously achieve the neighborhood aggregation of
intra-frame nodes and message passing among inter-frame graphs.
  Finally, we propose a dual-attention method consisting of node-attention and
time-attention to obtain the temporal graph representation from the node
embeddings, where the self-attention mechanism is employed to learn the
importance of each node and each frame.
  We verify the proposed method on three video-based ReID datasets, i.e., Mars,
DukeMTMC and iLIDS-VID, whose experimental results demonstrate that the learned
pose feature can effectively improve the performance of existing appearance
models.
