  Video-based person re-identification (re-ID) is an important technique in
visual surveillance systems which aims to match video snippets of people
captured by different cameras. Existing methods are mostly based on
convolutional neural networks (CNNs), whose building blocks either process
local neighbor pixels at a time, or, when 3D convolutions are used to model
temporal information, suffer from the misalignment problem caused by person
movement. In this paper, we propose to overcome the limitations of normal
convolutions with a human-oriented graph method. Specifically, features located
at person joint keypoints are extracted and connected as a spatial-temporal
graph. These keypoint features are then updated by message passing from their
connected nodes with a graph convolutional network (GCN). During training, the
GCN can be attached to any CNN-based person re-ID model to assist
representation learning on feature maps, whilst it can be dropped after
training for better inference speed. Our method brings significant improvements
over the CNN-based baseline model on the MARS dataset with generated person
keypoints and a newly annotated dataset: PoseTrackReID. It also defines a new
state-of-the-art method in terms of top-1 accuracy and mean average precision
in comparison to prior works.
