  Person re-identification has become a very popular research topic in the
computer vision community owing to its numerous applications and growing
importance in visual surveillance. Person re-identification remains challenging
due to occlusion, illumination and significant intra-class variations across
different cameras. In this paper, we propose a multi-task network base on an
improved Res2Net model that simultaneously computes the identification loss and
verification loss of two pedestrian images. Given a pair of pedestrian images,
the system predicts the identities of the two input images and whether they
belong to the same identity. In order to obtain deeper feature information of
pedestrians, we propose to use the latest Res2Net model for feature extraction
of each input image. Experiments on several large-scale person
re-identification benchmark datasets demonstrate the accuracy of our approach.
For example, rank-1 accuracies are 83.18% (+1.38) and 93.14% (+0.84) for the
DukeMTMC and Market-1501 datasets, respectively. The proposed method shows
encouraging improvements compared with state-of-the-art methods.
