Person Re-Identification With Coarse-To-Fine Visual Attention

The basic goal of person re-identification (re-id) tasks is to identify a person across disjoint camera views. It has been deeply explored in video surveillance but still remains a very challenging problem. In this paper, we introduce a novel model for re-id tasks with two components: an expressive feature fusion strategy that consists of high-level convolution features and the low-level optical information, and an improved recurrent attention model that performs a coarse to-fine feature selection. To the best of our knowledge, experiments show that our model achieves the best performance on several benchmark datasets compared with all the other state-of-the-art approaches.