  Person re-identification (ReID) is an important problem in computer vision,
especially for video surveillance applications. The problem focuses on
identifying people across different cameras or across different frames of the
same camera. The main challenge lies in identifying the similarity of the same
person against large appearance and structure variations, while differentiating
between individuals. Recently, deep learning networks with triplet loss have
become a common framework for person ReID. However, triplet loss focuses on
obtaining correct orders on the training set. We demonstrate that it performs
inferior in a clustering task. In this paper, we design a cluster 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 and can achieve higher accuracy on the test set
especially for a clustering task. We also introduce a batch hard training
mechanism for improving the results and faster convergence of training.
