Domain disentanglement and contrastive learning with source-guided sampling for unsupervised domain adaptation person re-identification

In recent years, fully supervised Person re-id methods have already been well developed. Still, they cannot be easily applied to real-life applications because of the domain gap between real-world databases and training datasets. And annotating ground truth label for the entire surveillance system with multiple cameras and videos are labor-intensive and impracticable in the real application. Besides, as the awareness of the right to privacy is rising, it becomes more challenging to collect sufficient training data from the public. Thence, the difficulty of constructing a new dataset for deployment not only arises from the labor cost of labeling but also because the raw data from the public are hard to come by. To be better adapted to real-life system deployment, we proposed an unsupervised domain adaptation based method, which involves Domain Disentanglement Network and Source-Guided Contrastive learning (SGCL). DD-Net first narrows down the domain gap between two datasets, and then SGCL utilizes the labeled source dataset as the clue to guide the training on the target domain. With these two modules, the knowledge transfer can be completed successfully from the training dataset to real-world scenarios. The conducted experiment shows that the proposed method is competitive with the state-of-the-art methods on two public datasets and even outperforms them under the setting of the small-scale target dataset. Therefore, not only the Person Re-ID, but also the object tracking in video or surveillance system can benefit from our new approach when we went to deploy to different environments.