Research And Application Of Pedestrian Recognition Algorithm Based On Local Features And Adversarial Generation

With the development of technology and the popularization of cameras in daily life,intelligent video surveillance systems play an increasingly important role in maintaining the stability and security of cities.Various methods of using cameras to monitor and investigate crimes are emerging.Due to the limit of camera resolution and shooting angle,high-quality facial photos are usually not obtained using surveillance cameras.In the case of face recognition failure,pedestrian re-recognition is a very important alternative method.Pedestrian rerecognition technology establishes a correspondence relationship between cross-camera and cross-scenario pedestrian images,and tracks personnel information according to their clothing,hair style,body shape and other characteristics.The images collected by monitoring often come from multiple different cameras.Due to the different camera placement angles,parameter settings,shooting scenes,and shooting times,the style,lighting,perspective,and posture of the same pedestrian under different cameras vary greatly.The research on pedestrian reidentification faces many challenges.Based on computer vision technology,this article uses local features and generative adversarial algorithms to search for the same pedestrian pictures under different cameras,to achieve accurate searching and Location of target person and to meet the requirements of intelligent video surveillance system for pedestrian re-identification.The main content of this paper is the research and application of pedestrian re-identification algorithm based on local features and generative adversarial networks.The main work is as follows:First of all,the research background of this paper is introduced,and the significance of this topic is put forward for the problems existing in traditional pedestrian re-identification algorithms.Subsequently,the benchmark dataset and evaluation indicator of pedestrian reidentification are introduced in details,and the dataset and evaluation standards used in the experiments of this paper are briefly explained.Secondly,the pedestrian re-recognition algorithm based on local features is studied for cross-camera recognition.According to the characteristics of pedestrian images,this paper improves the pedestrian re-identification algorithm based on global and local fine-grained features.The global and local branches of the network are used to extract the features of pedestrians in different granularity.A global branch consists of three-granularity branch and four-granularity branch which are innovative structure of local branches are used.The triplethard loss and the center loss are added to the loss function to constraints the intra-class and outer-class distance,and the re-ranking technique is added to improve the accuracy of searching and sorting.The granularity extraction suitable for pedestrian characteristics simplifies the process of network feature extraction,improves the learning efficiency,and obtains excellent performance.Thirdly,on this basis,the dataset picture is expanded based on the method of adversarial generative network.According to the structure coding and appearance coding of the pedestrian image,high-quality cross-identity composite images are generated by switching the appearance or structure coding to enhance the training dataset,and the generated images are fed back online to the appearance encoder for improving the discrimination module.The improved pedestrian re-recognition method of joint generative and discriminative learning uses the WGAN network whose main body is the WGAN,adjusts the convolutional and residual structure in the encoder,and introduces the ASPP structure to extract features of different scales in time.Adjusting the up-sampling structure in the generator can restore more details of the image.With the help of the multi-scale patch GAN idea,the discriminator performs joint features on multiple sizes of the image.Through the above improvements,the overfitting problem is alleviated,and a dataset that meets the training standards is generated.Finally,the implementation and experimental results of the pedestrian re-identification algorithm based on local features and generative adversarial network are compared and analyzed.The characteristics of the two algorithms and the applicable scenarios of the two models are summarized.