Conference on Electronic Information Engineering and Data Processing最新文献

{"title":"Research on UAV video motion target detection method based on optical flow network","authors":"Zhuoyao Li, Xi Wu, Jinrong Hu, Ye Zhu, Ying Fu","doi":"10.1117/12.2682458","DOIUrl":"https://doi.org/10.1117/12.2682458","url":null,"abstract":"Motion target detection is a prerequisite for road monitoring, motion target tracking, instance segmentation and other tasks. UAV video images are easily affected by some unavoidable factors in the acquisition process, such as wind interference and own motion during the shooting process can lead to image background changes, target scale changes and intermittent motion, making the motion target detection task more challenging. To address the problems of poor accuracy of existing UAV video motion target detection methods based on deep optical flow networks and the limitation of target detection performance in complex scenes due to the complex and diverse features of UAV video data, this paper proposes a new UAV video motion target detection method based on optical flow networks. Firstly, a convolutional structure reparameterization method is used in the coding part to further fuse detailed and semantic information to improve the feature expression capability of video images; secondly, the self-attentive global motion feature enhancement module proposed in this paper is introduced to improve the network's ability to extract global information and better combine contextual information to achieve more accurate optical flow estimation; finally, the optical flow threshold segmentation is used to obtain different motion target detection results for different scenes by optical flow threshold segmentation. In this paper, three sets of low-altitude UAV video data from different scenes are selected for experiments on the public dataset AU-AIR2019, and the experimental results prove that the proposed method can achieve better motion target detection results in single-target, multi-target and occluded target scenes, and it is better than the current mainstream optical flow networks: FlowNet1, PWC-Net, HD3, PWC-Net and HD3 on the public dataset FlyingChairs. PWC-Net, HD3, GMA metrics EPE (end point error, EPE) on the public dataset FlyingChairs, and improves the RAFT by 0.10 over the benchmark network in this paper, which effectively improves the accuracy of UAV video motion target detection by deep optical flow networks.","PeriodicalId":177416,"journal":{"name":"Conference on Electronic Information Engineering and Data Processing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126946586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review rating model based on subjective vocabulary in user reviews","authors":"Fanxing Zeng","doi":"10.1117/12.2682344","DOIUrl":"https://doi.org/10.1117/12.2682344","url":null,"abstract":"A problem of using the user review for scoring restaurants from Yelp’s dataset of restaurant business is discussed. Reviews posted by users on the same product or service are diverse and subjective. The sentiment and focus of user reviews are more likely to be subjective even for the same product and service. A review rating model with subjective tendencies of users is constructed through using sentiment classification and cluster analysis to analyze the subjective vocabularies and sentiment coefficients in reviews. The model identifies the terms of different categories in user reviews, quantifies and analyzes them, combines the sentiment with categories, and finally selects the rating of the restaurant as the dependent variable and the elements including the food quality, the restaurant ambience, the service and the grade of recommendation as independent variables before constructing user ratings by using a traditional least squares multiple linear regression model.","PeriodicalId":177416,"journal":{"name":"Conference on Electronic Information Engineering and Data Processing","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127049259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on improving pedestrian detection algorithm based on YOLOv5","authors":"Xiaogang Lin, Anjun Song","doi":"10.1117/12.2682285","DOIUrl":"https://doi.org/10.1117/12.2682285","url":null,"abstract":"We proposed a pedestrian detection algorithm combining YOLOv5 with convolution and channel attention mechanism. First, we use our own pedestrian dataset to train the YOLOv5 detection model. Then, three attention mechanisms, SE, CBAMC3, and CoordAtt, are used to enhance the detection performance. The experiment demonstrated that the precision of both SE and CoordAtt decreased, the recall of SE also decreased, while the accuracy of CBAMC3 was improved and the mAP changed little, thus CBAMC3 became the best model for pedestrian detection. Research indicates that adding convolution block attention modules can increase the precision of detecting small pedestrian targets.","PeriodicalId":177416,"journal":{"name":"Conference on Electronic Information Engineering and Data Processing","volume":"12700 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131149913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A method to improve Ripley's function to analyze spatial pattern by factor analysis","authors":"Hongchun Qu, Qinhao Zhang","doi":"10.1117/12.2682259","DOIUrl":"https://doi.org/10.1117/12.2682259","url":null,"abstract":"The Ripley function is a spatial point pattern analysis method, which is used to characterize point processes at different distance scales. However, to use the spatial point pattern analysis in combination with other spatial pattern analysis methods, or to analyze the spatial pattern of a population and individuals, if the spatial pattern is affected by many influencing factors, the introduction of the dimension of scale in the subsequent analysis will lead to a large number of dimensions to be examined and lack of correlation, which makes the study impossible. Therefore, in this study, the Ripley's function was improved by reducing the dimensionality of the calculated results through factor analysis, so that the results obtained from Ripley's K function only show one spatial pattern, thus eliminating the influence of scale on the spatial pattern, and expressing the spatial pattern that best reflects the current population or individual. Four standard spatial point pattern datasets were used to verify the ability of factor analysis to respond to spatial patterns after dimensionality reduction for Ripley'K, Ripley'L, and Ripley'G. The results showed that for aggregated point patterns the accuracy of the calculated results after dimensionality reduction reached 100%, where For the random point pattern, the average accuracy is also above 95%, which indicates that the applicability of factor analysis dimensionality reduction to spatial pattern models is in the random point pattern and aggregation pattern. In terms of different scale point sets, it has good adaptability to all three scale point set sizes. In this way the application scenario of Ripley function will be more extensive.","PeriodicalId":177416,"journal":{"name":"Conference on Electronic Information Engineering and Data Processing","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114947075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A vector compression coding algorithm based on SIMD(VCABSIMD)","authors":"Hu Wang, Jingsha He, Nafei Zhu, Haiyang He","doi":"10.1117/12.2682252","DOIUrl":"https://doi.org/10.1117/12.2682252","url":null,"abstract":"In the vector operation system, each data operation will only increase or decrease the same number for each component of the vector, that is to say, the vector is regarded as a data object on the whole, and each operation is carried out on the whole. For example, translation, scaling and other operations in computer graphics. Such computing systems are currently widely used in graphics, machine learning, mathematical modeling and other fields. When the data in the vector computing system is homomorphic encrypted and uploaded to the cloud server, the problem of low vector coding efficiency and polynomial coefficient expansion caused by a large number of polynomial operations is generated. The existing SIMD coding algorithm encodes an n-dimensional vector integer into a polynomial. The number of calculations increases exponentially with the increase of n, and the coding efficiency of the vector is very low. For typical FHE schemes such as BGV, BFV and CKKS, the main performance bottleneck comes from a large number of polynomial algorithms. Specifically, encrypted data is typically composed of a pair of polynomials with coefficients of hundreds or thousands of bits, requiring expensive multiword arithmetic. In addition, large polynomial lengths increase the computational complexity. Therefore, in this paper, a vector compression coding algorithm based on SIMD(VCABSIMD) is proposed to solve the performance problem when the data in the vector computing system is homomorphically encrypted and uploaded to the cloud server for calculation. The vector coding efficiency and polynomial calculation are deeply optimized.","PeriodicalId":177416,"journal":{"name":"Conference on Electronic Information Engineering and Data Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115047934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abnormal sound detection based on composite autoencoder Gaussian mixture model","authors":"Heng Wang, Jie Liu, Shuaifeng Li","doi":"10.1117/12.2682257","DOIUrl":"https://doi.org/10.1117/12.2682257","url":null,"abstract":"Aiming at the problem that the accuracy of abnormal sound detection under unsupervised conditions is not ideal, a novel abnormal sound detection model using composite self-coder combined with Gaussian mixture model is proposed. Firstly, the timing structure and gating mechanism of LSTM are used to improve the feature extraction ability of self-coder (including self-coder and variational self-coder), Secondly, Gaussian Mixture Model (GMM) is used to generate artificial data to improve the robustness of the self-coder against background noise. Experiments are carried out using ToyADMOS and MIMII public data sets, and the results are superior to the naive self-coder and the two improved self-coding models. On the six machines of the experimental data set, AUC increases by 6.34%, 6.65%, 4.03%, 5.57%, 2.38% and 1.07% respectively.","PeriodicalId":177416,"journal":{"name":"Conference on Electronic Information Engineering and Data Processing","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122892400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image stitching based on deep learning","authors":"Yongjun Zhang, Xueqing Wang","doi":"10.1117/12.2682403","DOIUrl":"https://doi.org/10.1117/12.2682403","url":null,"abstract":"An efficient unsupervised deep learning image stitching algorithm using YOLOv5. The method uses a feature pyramid structure for homography and non-local blocks combined with reconstruction for image fusion. Simulation experiments show that the PSNR and SSIM of the method are significantly improved. The artifacts are significantly reduced.","PeriodicalId":177416,"journal":{"name":"Conference on Electronic Information Engineering and Data Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121432421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved reverse-time migration for electromagnetic waves imaging","authors":"Yu Wei, Dajun Chen, L. Kuang","doi":"10.1117/12.2682292","DOIUrl":"https://doi.org/10.1117/12.2682292","url":null,"abstract":"Reverse Time Migration (RTM) is one of the main seismic migration imaging algorithms, which has the advantages of high imaging accuracy and being unrestricted by tilt angle. In this paper, RTM based on cross-correlation imaging condition is used for electromagnetic waves imaging. After reducing low frequency noise using wavefield decomposition, the results of electromagnetic waves imaging still contain noise caused by multipath effects and clutter from the background. In order to reduce the effects of noises, we propose an improved RTM imaging method based on singular value decomposition (SVD). The numerical results show that the proposed method can reducing multipath noise and background clutter effectively by RTM combing wavefield decomposition with SVD. Electromagnetic wave imaging quality is improved.","PeriodicalId":177416,"journal":{"name":"Conference on Electronic Information Engineering and Data Processing","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122676346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient multi-dimensional dynamic aggregation scheme for location security","authors":"Bolun Li, Yajing Liu","doi":"10.1117/12.2682271","DOIUrl":"https://doi.org/10.1117/12.2682271","url":null,"abstract":"The statistics on demand of customer is very important for business, such as collecting location information of customers to provide them with various services. In this paper, we present a new method for customers to authenticate their identity and position without revealing them and statistics the distribution of passenger flow in different periods, where the purpose of privacy-preserving could be achieved. Due to the identify of customers changes rapidly, in order to ensure that the identify of customers can be updated in time, this paper use dynamic group signature method to add and delete identify frequently. In the meantime, customers need to be divided into different types to meet the needs of different business. Therefore, we will use customers' age and occupation as their identify marks. In order to understand the needs of customers of different ID, age and occupations that we statistics the identity to help business find the suitable development areas.","PeriodicalId":177416,"journal":{"name":"Conference on Electronic Information Engineering and Data Processing","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126153302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A study on finite false target jamming for radar track generation","authors":"Yanqi Wang, Chao Wang, Qingzhan Shi, Jingjian Huang, N. Yuan","doi":"10.1117/12.2682454","DOIUrl":"https://doi.org/10.1117/12.2682454","url":null,"abstract":"To solve the problem that radar can still generate effective tracks to the covered target under the existing false target jamming technology, a method of jamming radar target track generation using finite false target signals is proposed through the plot and track correlation process of the radar in the initial stage of target searching and tracking. Through electronic reconnaissance, interception, and retransmission of radar signals by a jammer, a finite number of false target signals with random movement characteristics are generated around the covered target, interfering with track correlation during target data processing by radar. Simulation examples demonstrate the effectiveness of the proposed method.","PeriodicalId":177416,"journal":{"name":"Conference on Electronic Information Engineering and Data Processing","volume":"120 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126307038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}