  Deep learning technology promotes the rapid development of person
re-identifica-tion (re-ID). However, some challenges are still existing in the
open-world. First, the existing re-ID research usually assumes only one factor
variable (view, clothing, pedestrian pose, pedestrian occlusion, image
resolution, RGB/IR modality) changing, ignoring the complexity of multi-factor
variables in the open-world. Second, the existing re-ID methods are over depend
on clothing color and other apparent features of pedestrian, which are easily
disguised or changed. In addition, the lack of benchmark datasets containing
multi-factor variables is also hindering the practically application of re-ID
in the open-world. In this paper, we propose a low-cost and high-efficiency
method to solve shortcomings of the existing re-ID research, such as unreliable
feature selection, low efficiency of feature extraction, single research
variable, etc. Our approach based on pose estimation model improved by group
convolution to obtain the continuous key points of pedestrian, and utilize
dynamic time warping (DTW) to measure the similarity of features between
different pedestrians. At the same time, to verify the effectiveness of our
method, we provide a miniature dataset which is closer to the real world and
includes pedestrian changing clothes and cross-modality factor variables
fusion. Extensive experiments are conducted and the results show that our
method achieves Rank-1: 60.9%, Rank-5: 78.1%, and mAP: 49.2% on this dataset,
which exceeds most existing state-of-art re-ID models.
