关键词: 行人再识别, 度量学习, 迁移学习

Abstract: Pedestrian re-recognition is a challenging task in the field of computer vision. This task focuses on the appearance change pattern of individuals. Due to the drastic variation of appearance feature, there is small sample problem in metric learning for person re-identification. In this paper, a transfer metric learning based method is proposed. By minimizing the difference between the distribution of source data and target data, the proposed method achieves the transform of metric model from source dataset to target dataset. The proposed method not only enhances the diversity of training samples which improves the discrimination of metric model, but also improves its generalization. Finally, the effectiveness and accuracy of the proposed method are verified on the VIPeR and CUHK01 datasets by the pre-training on iLIDS dataset.

Key words: person re-identification, metric learning, transfer learning