A GENERAL RE-RANKING METHOD BASED ON METRIC LEARNING FOR PERSON RE-IDENTIFICATION

When Person Re-identification is considered as a retrieval task, re-ranking becomes a critical part of improving the reidentification accuracy. Most of the existing re-ranking methods focus on k-nearest neighbors, which requires a lot of queries and memory. In this paper, we propose a Feature Relation Map based Similarity Evaluation (FRM-SE) model to tackle this problem. The Feature Relation Map is utilized to automatically mine the latent relation between the k-neighbors through convolution operation. The re-ranking distance is learned through the FRM-SE model with metric learning. Further, we optimize the existing re-ranking method to utilize the advantage of the FRM-SE model for maintaining a balance between accuracy and complexity.
The proposed approach is validated on two benchmark datasets, Market1501 and CUHK03. Results show that our reranking method is superior to the state-of-the-art re-ranking methods. Furthermore, in the transfer learning setting, the model trained on either Market1501 or CUHK03 can achieve a comparable accuracy improvement on the DuekMTMC dataset, which validates the generalization of our SE model.