The Person Re-identification Algorithm Based On Domain Adaptation Transfer Learning

The person re-identification(Re-ID)task aims at retrieving the pedestrian images with the same identity as the query image but taken by different cameras in the gallery.Re-ID plays an important role in intelligent video surveillance,criminal investigation,smart city and many other fields.With the development of deep learning approaches,supervised Re-ID models have achieved satisfactory performance on the labeled benchmark dataset.However,the supervised methods require sufficient manually tagged labels,which is too expensive in the real-world applications.Recently,many works employ deep learning based unsupervised domain adaptation transferring learning methods to the study of cross domain Re-ID.The label estimation methods have achieved relatively superior results,but there is still a huge gap compared with the supervised learning method.The key of the domain adaptation label estimation approaches is how to improve the reliability of the generated pseudo labels,and then the Re-ID model is trained by using the pseudo labels to adapt to the target domain.To generate more reliable pseudo labels,this paper proposes a discriminative learning network with target domain latent information(LatentDLN)for exploring three types of latent information in the target domain,i.e.,the valid region information,the nearest neighbor information and the camera style information.First,this paper adopts key points detection for extracting the valid local regions of the person image,then a multi-branch network is designed to leverage the local and global cues,which improves the discriminative ability of feature representation.Second,we use the re-ranking mechanism based on the nearest neighbor information to improve the quality of the ranking list in the process of distance measurement.Third,to utilize the camera information,this paper trains a multi-domain image translation generative adversarial network to obtain camera style transferred images for each target image.The pseudo labels of true images estimated by the unsupervised clustering method are assigned to the corresponding generated images for data augmentation during the training process.Furthermore,to deal with the cases of feature missing caused by invalid regions,we propose a heuristic distance metric learning method to effectively evaluate the similarity between different images.Finally,the proposed LatentDLN is trained based on the self-training mechanism,i.e.,we repeatedly and alternatively conduct the label estimation and the training process until the model is stable.This paper proposes a region-guided jointly supervised learning strategy to improve the feature representation ability of the initial model in the target domain,which further utilizes the latent valid region and camera style information.The initial model jointly trained on the source and the target domain could to some extent capture the local and global cues and the camera variances in the target domain.It provides more accurate labels at the beginning phase of the self-training process and improves the final performance of the proposed model.In this paper,we conduct extensive experiments on three large-scale standard Re-ID datasets,i.e.,Market-1501,DukeMTMC-reID and MSMT17.Experimental results show that the proposed method significantly outperforms the state-of-the-art approaches,which demonstrates the effectiveness of the proposed method for cross-domain Re-ID.