# A Domain Adaptive Person Re-Identification Based on Dual Attention Mechanism and Camstyle Transfer
## 1 Introduction  
Person re-identification (re-ID) is not only a hot research topic, but also has considerable practical value in computer vision. Given an interested query pedestrian, person re-ID focuses on matching the same pedestrian in a camera network without any overlapping area [1, 2, 3, 4]. Following the development of deep learning, supervised person re-ID methods have already achieved high rank-1 accuracy and mAP accuracy on widely used datasets [5, 6, 7]. However, these methods heavily rely on a large amount of labeled information in the target domain dataset. It is often difficult to obtain labeled information. Various unsupervised person re-ID methods have been proposed to overcome the lack of labeled information [8, 9, 10, 11]. For example, the labeled source domain dataset and the unlabeled target domain dataset are used to train the model, or the unlabeled target domain dataset is directly used to train the model. Existing unsupervised person re-ID methods can be divided into three categories: (a) Domain adaptation is used to align the feature distribution between source domain and target domain [12, 13, 14]. (b) Generative adversarial network (GAN) is used to realize the transformation of pedestrian image style, while the identity annotations of source domain images are retained [15, 16, 17]. (c) Generated pseudo-labels on the target domain are used for training, and pseudo labels are assigned to similar images by different methods, such as clustering and KNN search [18, 19, 20, 21]. The methods of the third category are restricted by pseudo labels and has poor accuracy in actual application scenarios. Therefore, this article focuses on the first two categories. The corresponding methods of the first two categories use labeled data on the source domain. So, they can be regarded as transfer learning. Given labeled source data and unlabeled target data, cross-domain re-ID methods are dedicated to learning a model that adapts to the target domain. For cross-domain person re-ID methods, there is no overlap between labeled source domain data and unlabeled target domain data. Both the source domain and target domain have completely different classes (pedestrian identities). In addition, since the imaging styles of different cameras are different, each domain can be further divided into different sub-domains. So, poor migration performance is attributed to two main factors: intra-domain difference and inter-domain conversion. In order to achieve good migration performance, both factors should be considered in the design process. The intra-domain difference is mainly caused by the inconsistency of camera parameters. However, a lot of existing work ignores the first factor. In fact, in the same target domain, the images captured by different cameras still have obvious style differences. The images captured by one camera can be regarded as a sub-domain of the target domain. The commonly used datasets DukeMTMC-ReID and Market1501 have eight and six sub-domains, respectively, which can be derived from the corresponding camera numbers. In a real-world scene, the distribution of a sub-domain may be considerably different from the distribution of other sub-domains, due to different camera types and image collection scenes. In this case, it is not appropriate to treat any target domain as a whole. It is better to reduce the deviation between each sub-domain in the source and target domains to achieve domain adaptation. In order to alleviate the above issue, [PERSON] (2017) applied CycleGAN (CamStyle) to generate camera style conversion images to achieve data enhancement for person re-ID. CycleGAN is used to train an image-to-image conversion model for each pair of cameras. Subsequently, the obtained model can generate new image samples that are converted from the source domain to target domain. Since CycleGAN can only be modeled by a one-to-one domain mapping, this method can only learn the mapping between a pair of cameras used in one model.