  Current object re-identification (ReID) system follows the centralized
processing paradigm, i.e., all computations are conducted in the cloud server
and edge devices are only used to capture and send images. As the number of
videos experiences a rapid escalation, this paradigm has become impractical due
to the finite computational resources. In such a scenario, the ReID system
should be converted to fit in the cloud-edge collaborative processing paradigm,
which is crucial to boost the scalability and practicality of ReID systems.
However, current relevant work lacks research on this issue, making it
challenging for ReID methods to be adapted effectively. Therefore, we pioneer a
cloud-edge collaborative inference framework for ReID systems and particularly
propose a distribution-aware correlation modeling network (DaCM) to make the
desired image return to the cloud server as soon as possible via learning to
model the spatial-temporal correlations among instances. DaCM embeds the
spatial-temporal correlations implicitly included in the timestamps into a
graph structure, and it can be applied in the cloud to regulate the size of the
upload window and on the edge device to adjust the sequence of images,
respectively. Traditional ReID methods can be combined with DaCM seamlessly,
enabling their application within our proposed edge-cloud collaborative
framework. Extensive experiments demonstrate that our method obviously reduces
transmission overhead and significantly improves performance. We will release
our code and model.
