  Person re-identification (PReID) has received increasing attention due to it
is an important part in intelligent surveillance. Recently, many
state-of-the-art methods on PReID are part-based deep models. Most of them
focus on learning the part feature representation of person body in horizontal
direction. However, the feature representation of body in vertical direction is
usually ignored. Besides, the spatial information between these part features
and the different feature channels is not considered. In this study, we
introduce a multi-branches deep model for PReID. Specifically, the model
consists of five branches. Among the five branches, two of them learn the local
feature with spatial information from horizontal or vertical orientations,
respectively. The other one aims to learn interdependencies knowledge between
different feature channels generated by the last convolution layer. The remains
of two other branches are identification and triplet sub-networks, in which the
discriminative global feature and a corresponding measurement can be learned
simultaneously. All the five branches can improve the representation learning.
We conduct extensive comparative experiments on three PReID benchmarks
including CUHK03, Market-1501 and DukeMTMC-reID. The proposed deep framework
outperforms many state-of-the-art in most cases.
