Abstract: Person re-identification aims to retrieve specific pedestrian targets from the target database under the condition of cross camera.It has important application value in the field of video surveillance.At present,the difficulty of the research is that the sample images have large intra class differences and small inter class differences.Therefore,how to design and train the deep neural network to extract a more discriminative feature from pedestrian images is the key.In this paper,we propose a network structure combining global features and local features learning,which can extract global features and local features simultaneously.In view of the different importance of each part of the local features to the description of pedestrian features,this paper proposes a fusion method of local features,which can adaptively generate the weight of each local feature.Finally,the local features and glo-bal features are combined to make the pedestrian features get more comprehensive representation.In addition,in view of the fuzzy optimization objective of the previous triple loss based on hard sample mining,this paper proposes an improved triple loss function based on hard sample mining.The effectiveness of the proposed method is verified on the mainstream person re-identification data sets Market-1501 and DukeMTMC-reID,respectively,and the mAP values are 82.16% and 74.02%,and the Rank-1 values are 92.75% and 86.8%,respectively.

Key words: Deep learning, Feature fusion, Person re-identification, Retrieval, Triplet loss