Research On Person Re-Identification Algorithm Based On Deep Learning

In recent years,with the popularity of video surveillance system,utilizing artificial intelligence technology to help the development of video surveillance has become a hot issue.With general application requirements in crime tracking,security against terrorism and so on,Person re-identification(ReID)is a crucial fundamental application research in the security monitoring field.Combining deep learning with computer vision technology is the most effective way to solve the problem of ReID.This paper is aimed at the application research requirements of ReID,focusing on the problem that the camera views and pedestrian pose variances have too much interference on ReID and the problem of the limited ability of model domain adaptation between different scenes and the high cost of manual labeling.The details are as follows:For the problem of camera views and pose variances,firstly our paper utilizes the spatial transformation network(STN)to adaptively adjust the variances to align the pedestrians,and then combine the low-level and high-level features of DenseNet architecture to obtain the distinguishable apparent features.Finally,the triplet loss could learn the metric distance in feature space,trying to pull closer the intra-class distance and push away the inter-class distance.The triplet loss avoids the interference of the number of categories on the model.Combining spatial transformation network,DenseNet architecture and triple loss,an end-to-end robust and efficient ReID system is implemented.Although our ReID system alleviates the problems of camera views and pedestrian pose variances to a certain extent,there are still shortcomings for drastic pedestrian posture changes in sports scenes.From the perspective of features,a highly distinguishable feature can overcome the noise caused by the change of pedestrian posture.This paper proposes a deep mutual information discriminative network for pedestrian feature enhancement,therefore achieving distinguishable pedestrian features.Experiments on two benchmark models prove the effectiveness of the supervised feature enhancement algorithm.In this paper,a large number of unlabeled pedestrian images are used to mitigate domain adaptation problems in ReID issue.The supervised training model parameters are adopted as the initial parameters in unsupervised learning.The discriminator network maximizes the mutual information between the input images and features to enhance the feature representation,therefore gradually adapting to the new scene dataset,which alleviates the problem of unsupervised domain adaptation.The experimental results show that the proposed pedestrian alignment algorithm and unsupervised feature enhancement algorithm are feasible,which can effectively solve the pose variance and domain adaptation problem in ReID task.It has certain theoretical value for the in-depth study and industrial application of ReID method.