Occluded person re-identification is one of the challenging areas of computer
vision, which faces problems such as inefficient feature representation and low
recognition accuracy. Convolutional neural network pays more attention to the
extraction of local features, therefore it is difficult to extract features of
occluded pedestrians and the effect is not so satisfied. Recently, vision
transformer is introduced into the field of re-identification and achieves the
most advanced results by constructing the relationship of global features
between patch sequences. However, the performance of vision transformer in
extracting local features is inferior to that of convolutional neural network.
Therefore, we design a partial feature transformer-based person
re-identification framework named PFT. The proposed PFT utilizes three modules
to enhance the efficiency of vision transformer. (1) Patch full dimension
enhancement module. We design a learnable tensor with the same size as patch
sequences, which is full-dimensional and deeply embedded in patch sequences to
enrich the diversity of training samples. (2) Fusion and reconstruction module.
We extract the less important part of obtained patch sequences, and fuse them
with original patch sequence to reconstruct the original patch sequences. (3)
Spatial Slicing Module. We slice and group patch sequences from spatial
direction, which can effectively improve the short-range correlation of patch
sequences. Experimental results over occluded and holistic re-identification
datasets demonstrate that the proposed PFT network achieves superior
performance consistently and outperforms the state-of-the-art methods.

我们提出了一种基于局部特征变换器的人员再辨识框架（PFT），通过三个模块设计，在提高视觉变换器的效率的同时最大限度提取局部特征，实现了对局部遮挡情况下的人员再辨识。实验结果显示，PFT 网络在遮挡和整体再识别数据集上表现出优异性能，并优于现有技术。