DCN-Based unsupervised domain adaptive person re-identification method

In order to solve the problems of occlusion, large differences in styles between domains and cameras in the research of unsupervised person re-recognition, this paper proposes an unsupervised domain adaptive model based on deformable convolution. Aiming at the occlusion problem in the feature extraction process, a CNN model based on deformable convolution is proposed. In the pre-training stage, it is proposed to apply SPGAN to directly reduce the difference between domains. During the training process, it is proposed to use CycleGAN to generate images of different camera styles to alleviate the problem of camera style differences. A multi-loss collaborative training method is proposed to realize the iterative optimization of CycleGAN and re-used CNN models to further improve the recognition accuracy. The experimental results show that the method proposed in this paper is tested in the source domain DukeMTMC-reID/Market-1501 and the target domain Market-1501/DukeMTMC-reID, and mAP and Rank-1 reach 68.7%, 64.1% and 88.2%, 78.1%, respectively. The model proposed in this paper effectively alleviates the problems of pedestrians being occluded, and large differences in styles between domains and cameras. Compared with the existing methods, it has a better recognition effect.