# Feature-Level Camera Style Transfer for Person Re-Identification
## 1 Introduction  
Person re-ID [1] aims to match images of the same person appearing in different camera views. Due to the camera style variance in the surveillance system, images of the same pedestrian often show significant differences in pose, illumination and background, which increase the intra-class variations in the model and harm its retrieval performance. Thus, camera style variance is a major difficulty in the person re-ID task. With the rapid development of deep neural networks in the past decade, researchers have explored various deep learning-based methods for person re-ID [2]. Many researchers have tried to solve camera style variance as a part of the optimization problem in a common classification model. Some researchers focus on the innovation of deep learning loss functions. [PERSON] et al. [3, 4] study the extension of classification losses based on softmax, while [PERSON] et al. [5] and [PERSON] et al. [6] investigate the usage of contrastive losses based on hard sample mining. [PERSON] et al. [7] and [PERSON] et al. [8] pay attention to the modification of the base model, which extracts pedestrians' features. However, these methods sidestep the camera style variance that innately exists in re-ID datasets. Therefore, they cannot solve the camera style variance problem fundamentally. Another prevalent choice is using generative models to make camera style-guided image-level data augmentation. Benefiting from the augmented dataset, the re-ID model can directly perceive these variations. With the rapid progress of the GAN theory [9; 10; 11; 12], many GAN-based practices [5; 13; 14] have been proven as helpful alliances in re-ID tasks. However, these methods are coupled with off-the-backbone generative networks which cost tremendous time and memory in the training process. Taking CamStyle [14] as an example, the training process of the person re-ID model on the extended dataset only requires about one hour. However, it takes a couple of days to train the generative model, which is indicated as a bunch of CycleGANs [15] in this task. Furthermore, the introduction of a complex generation network splits the training process of the entire model, and it makes it hard to achieve an optimal for both models simultaneously. In order to overcome camera style variance in the person re-ID problem, a feature-level Camera Style Transfer (CST) approach is proposed to serve as data augmentation with low time and memory cost. The authors propose utilizing an additional camera style feature extractor to learn camera-related information. The proposed CST approach then takes source identity features and target camera style features as input, and it generates features that transfer their camera information while maintaining their identity information. An Adaptive Batch Normalization (ABN) layer is designed to inject camera information into the original identity feature. Compared with image-level GAN-based methods, the proposed CST model can produce high-quality features precisely and speedily. Moreover, extensive experiments show that the proposed CST model can adapt to different baselines and metric learning methods, proving the universality of the proposed method. Finally, a lightweight framework is presented, where the generative model is embedded into a person re-ID model in an end-to-end manner. In summary, this paper makes the following main contributions. * To overcome camera variations, a feature-level Camera Style Transfer (CST) model for person re-ID is proposed, acting as a data augmentation approach which benefits the training process of re-ID model. * To achieve camera style transfer while preserving the identity information, an Adaptive Batch Normalization (ABN) layer is designed to inject camera information and produce high-quality features. * To optimize the generative model and re-ID model simultaneously, a lightweight framework is proposed to train the generative model and re-ID model in an end-to-end manner. The rest of this paper is organized as follows: Related work is discussed in Section 2.