Research On Person Re-identification Technology Based On Deep Learning

With the development of the current times,more and more public have paid attention to the social security.As a significant means of preventing and controlling public crimes,urban intelligent monitoring is playing an increasingly prominent role in public place management,large-scale case detection and suspect tracking.As one of the main research directions in the field of intelligent security,Person re-identification is a technology that using computer vision algorithms to find specific pedestrians in a given image set or video sequence.However,due to the complexity of the practical application scenario,it is difficult to extract the effective features of pedestrians with discrimination,which brings great challenges to the task of person re-identification.In addition,the problem that the traditional retrieval method can not meet the task requirements of massive video data is gradually emerging.As a result,how to locate the target pedestrian accurately and quickly becomes an urgent problem to be solved.The deep learning methods can adaptively learn the effective features of the image,so avoiding the disadvantages of manual design and selection of features,and can adapt to the data volume requirements of the big data times.Therefore,this thesis uses the method based on deep learning to study the person re-identification technology from the following aspects:First,for the problem that the images in most pedestrian datasets is insufficient,this thesis proposes a data augmentation model named Person GAN which based on the generative adversarial networks.The model is based on Cycle GAN and the network is modified to adopt to the needs of pedestrian recognition tasks.At the same time,this thesis designs an identity mapping loss to constraint network model,so as to generate pedestrian images with different camera styles.Person GAN can not only effectively expand the dataset,but also can generate some samples that are more difficult to identify,so that providing a new momentum for training,and making the model more robust and widely applicable.:Second,in view of the problem that it is difficult to extract the discriminative features of pedestrians,this thesis proposes a Robust Joint Learning Networks(RJLN).RJLN is a network model based on representation learning.Firstly,the low-level features of pedestrians are extracted through the basic network.Then the improved global branch network and the improved local branch network are used to extract the global features and local features of the target pedestrians respectively,so as to adapt to extract different granularity features and enhance the learning ability of the model,so that the identification accuracy and robustness of the model are significantly improved.Finally,the loss function of the two branch networks is merged as the overall loss function of RJLN.Third,aiming at the extraction of local features,a Strong Part-base Networks(SP_Net)with simple structure and excellent performance is proposed in this thesis.Different from the local feature extraction methods such as skeleton key point location and pose estimation,The model directly uses the horizontally partition stripes of the person feature map as the network input,and then learns the fine-grained features from the horizontal stripes through the global average pooling.In order to improve the identification accuracy and robustness of the model,a convolutional layer with a kernel size of 1×1 is added to the SP_Net for reducing the dimension,and the over-fitting is prevented by the dropout layer and the batch normalize layer.Through experiments on Market-1501,Duke MTMC-re ID,CUHK03 and other datasets,this thesis verifies the improvement of the model.The experimental results show that the improved methods has significantly higher rank-k accuracy and m AP on the three data sets,and outperforms existing deep learning methods.