Multi-orientation Partitioned Network for Person Re-identification

Abstract

Combining global features with local features is an important solution to improve discriminative performances in person re-identification (Re-ID) task.In the past,external information was used to locate regions with corresponding semantics,thus mining local information.Most of these methods are not end-to-end,so the training process is complex.To solve this problem,a multi-orientation partitioned network (MOPN) is proposed,which can effectively mine local information and combine global information with local information for end-to-end feature learning.The network has three branches:one for extracting global feature and two for mining local information.Without relying on external information,the algorithm divides pedestrians' images into hori-zontal and vertical stripes in different local branches respectively,so as to obtain different local feature representations.Plenty of experiments conducted on Market-1501,DukeMTMC-reID,CUHK03 and cross-modal dataset SketchRe-ID show that the proposed method has better overall performance than other comparison algorithms,and is effective and robust.

Keywords

• person re-identification|deep learning|multi-branch network|local feature|global feature