  In addition to considering the recognition difficulty caused by human posture
and occlusion, it is also necessary to solve the modal differences caused by
different imaging systems in the Visible-Thermal cross-modal person
re-identification (VT-ReID) task. In this paper,we propose the Cross-modal
Local Shortest Path and Global Enhancement (CM-LSP-GE) modules,a two-stream
network based on joint learning of local and global features. The core idea of
our paper is to use local feature alignment to solve occlusion problem, and to
solve modal difference by strengthening global feature. Firstly,
Attention-based two-stream ResNet network is designed to extract dual-modality
features and map to a unified feature space. Then, to solve the cross-modal
person pose and occlusion problems, the image are cut horizontally into several
equal parts to obtain local features and the shortest path in local features
between two graphs is used to achieve the fine-grained local feature alignment.
Thirdly, a batch normalization enhancement module applies global features to
enhance strategy, resulting in difference enhancement between different
classes. The multi granularity loss fusion strategy further improves the
performance of the algorithm. Finally, joint learning mechanism of local and
global features is used to improve cross-modal person re-identification
accuracy. The experimental results on two typical datasets show that our model
is obviously superior to the most state-of-the-art methods. Especially, on
SYSU-MM01 datasets, our model can achieve a gain of 2.89%and 7.96% in all
search term of Rank-1 and mAP. The source code will be released soon.
