Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification

Person re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on the training set. It still suffers from a weaker generalization capability from the training set to the testing set, thus resulting in inferior performance. In this paper, we design a quadruplet loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability…

• NN
  National Natural Science Foundation of China

  Awards: 61403383, 61673375
• NK
  National Key Research and Development Program of China

  Award: 2016YFB1001005