  Vehicle re-identification (Re-ID) involves identifying the same vehicle
captured by other cameras, given a vehicle image. It plays a crucial role in
the development of safe cities and smart cities. With the rapid growth and
implementation of unmanned aerial vehicles (UAVs) technology, vehicle Re-ID in
UAV aerial photography scenes has garnered significant attention from
researchers. However, due to the high altitude of UAVs, the shooting angle of
vehicle images sometimes approximates vertical, resulting in fewer local
features for Re-ID. Therefore, this paper proposes a novel dual-pooling
attention (DpA) module, which achieves the extraction and enhancement of
locally important information about vehicles from both channel and spatial
dimensions by constructing two branches of channel-pooling attention (CpA) and
spatial-pooling attention (SpA), and employing multiple pooling operations to
enhance the attention to fine-grained information of vehicles. Specifically,
the CpA module operates between the channels of the feature map and splices
features by combining four pooling operations so that vehicle regions
containing discriminative information are given greater attention. The SpA
module uses the same pooling operations strategy to identify discriminative
representations and merge vehicle features in image regions in a weighted
manner. The feature information of both dimensions is finally fused and trained
jointly using label smoothing cross-entropy loss and hard mining triplet loss,
thus solving the problem of missing detail information due to the high height
of UAV shots. The proposed method's effectiveness is demonstrated through
extensive experiments on the UAV-based vehicle datasets VeRi-UAV and VRU.
