Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things最新文献_第6页

Reidentifying soccer players in broadcast videos using Body Feature Alignment Based on Pose 基于姿态的身体特征对齐再识别广播视频中的足球运动员

Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things Pub Date : 2023-05-26 DOI: 10.1145/3603781.3603860

Sara Akan, Songül Varlı

{"title":"Reidentifying soccer players in broadcast videos using Body Feature Alignment Based on Pose","authors":"Sara Akan, Songül Varlı","doi":"10.1145/3603781.3603860","DOIUrl":"https://doi.org/10.1145/3603781.3603860","url":null,"abstract":"Re-identification (re-id) of people in images is a well-studied problem in computer vision for many applications. The re-identification of players in broadcast videos of team sports is the main subject of this work. We specifically concentrate on recognizing the same player in images taken at any given time during a match from various camera angles. Some significant differences exist between this task and other traditional person re-id applications, such as same team wear highly similar clothes, for each identification, there are only a small number of samples, and low resolutions of the images. One of the most difficult problems in object re-identification is extracting robust feature representation (ReID). Even though methods based on convolution neural networks (CNNs) have had significant success. But to improve extracting features, we present the novel approach Body Feature Alignment Based on Pose, utilizing pose landmarks to extract the image's useful information. During the feature constructing stage, our method makes use of human landmarks to obtain the angles and distances between the joints. According the results, the proposed method provide comparable improvements for convolutional networks.","PeriodicalId":391180,"journal":{"name":"Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things","volume":"79 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":"116310073","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}

引用次数: 0

Extended application of Small-Sample Information Diffusion* 小样本信息扩散的扩展应用*

Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things Pub Date : 2023-05-26 DOI: 10.1145/3603781.3603844

Shanshan Yuan

引用次数: 0

Multi-Resolution Diffeomorphic Image Registration with Convolutional Vision Transformer Network 基于卷积视觉变换网络的多分辨率差分图像配准

Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things Pub Date : 2023-05-26 DOI: 10.1145/3603781.3603849

Tao Xu, Ting Jiang, Haoyang Xing, Xiaoning Li

{"title":"Multi-Resolution Diffeomorphic Image Registration with Convolutional Vision Transformer Network","authors":"Tao Xu, Ting Jiang, Haoyang Xing, Xiaoning Li","doi":"10.1145/3603781.3603849","DOIUrl":"https://doi.org/10.1145/3603781.3603849","url":null,"abstract":"In recent years, the research of medical image registration based on convolutional neural network (CNN) has attracted much attention. In particular, the deformable image registration method based on diffeomorphism seems to have achieved promising results due to its unique topology conservation and transformation reversibility. However, the results of most existing learning-based approaches are not necessarily diffeomorphic. Moreover, due to local receptive fields caused by convolutional inductive bias, CNNs usually have limitations in catching the global and remote spatial relationships between points in anatomical images. Vision Transformer (ViT) shows tremendous advantages in modeling long-term dependencies in sequential images due to its embedded self-attention mechanism. Therefore, we propose a hybrid convolution Vision Transformer Network (CViT) model based on multi-resolution diffeomorphism. The model employs a multi-resolution strategy to learn global connectivity and local context of medical images in the diffeomorphic mapping space, which can simultaneously integrate the advantages of CNN and ViT to provide a better understanding of spatial correspondence. We evaluate our approach respectively on a large scale and a small scale dataset of 3D brain MRI scans, gaining an average Dice of 0.813 on the OASIS dataset. Extensive quantitative and qualitative results show that our method achieves state-of-the-art performance while maintaining desirable diffeomorphism.","PeriodicalId":391180,"journal":{"name":"Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things","volume":"5 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":"114926254","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}

引用次数: 0

Collaborative filtering hybrid recommendation algorithm incorporating knowledge graph 结合知识图谱的协同过滤混合推荐算法

Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things Pub Date : 2023-05-26 DOI: 10.1145/3603781.3603868

Qi Guo, Yong Shao, Changshun Yan, Yuliang Shi

引用次数: 0

Real time pulse compression processing technology based on FPGA 基于FPGA的实时脉冲压缩处理技术

Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things Pub Date : 2023-05-26 DOI: 10.1145/3603781.3603936

Mengrui Liu, Y. Deng, Wenliang Nie

{"title":"Real time pulse compression processing technology based on FPGA","authors":"Mengrui Liu, Y. Deng, Wenliang Nie","doi":"10.1145/3603781.3603936","DOIUrl":"https://doi.org/10.1145/3603781.3603936","url":null,"abstract":"Pulse compression is an indispensable processing technique for improving resolution in radar systems, and the general processing method for pulse compression is to complete fast Fourier transform (FFT) and other processing in an embedded processor, save the data in memory, and then complete data reading, imaging, and other processing by data processing software, Unable to achieve real-time processing of Frequency Modulation Continuous Wave (FMCW) signals. In this paper, a real-time pulse compression processing technology of FMCW signal field programmable gate array (FPGA) is proposed. First, the programmable logic (PL) end of FPGA receives data to realize zero filling, window function addition, pulse compression and scene interception processing in parallel pipeline mode, Finally, the processing results are transmitted to the upper computer through a gigabit network based on the User Datagram Protocol (UDP) protocol on the PL end and displayed in real-time. The experimental results show that the proposed data processing method can process 8192 data in 0.26 milliseconds, achieving real-time FMCW signal processing.","PeriodicalId":391180,"journal":{"name":"Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things","volume":"44 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":"126635170","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}

引用次数: 0

Deep Reinforcement Learning Based UAV Mission Planning with Charging Module 基于深度强化学习的无人机充电任务规划

Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things Pub Date : 2023-05-26 DOI: 10.1145/3603781.3603897

Yanfan Zhang, Hongyuan Zheng, X. Zhai

引用次数: 0

Object detection of inland waterway ships based on improved YOLOv7 基于改进YOLOv7的内河船舶目标检测

Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things Pub Date : 2023-05-26 DOI: 10.1145/3603781.3603848

Wei Guo, Z. Lv, Jin Li, Rui Chen

{"title":"Object detection of inland waterway ships based on improved YOLOv7","authors":"Wei Guo, Z. Lv, Jin Li, Rui Chen","doi":"10.1145/3603781.3603848","DOIUrl":"https://doi.org/10.1145/3603781.3603848","url":null,"abstract":"In recent years, with the rapid development of deep learning, more and more deep learning technologies have been applied to the field of ship detection. Compared with traditional target detection algorithms, deep learning target detection algorithms are more robust, have stronger generalization ability, and are easier to be applied to actual scenarios. On the premise of summarizing existing ship detection algorithms and based on the YOLOv7 detection framework, this paper aims at the characteristics of small target and high density of inland waterway ships in this paper. By introducing the improved K-Means++ anchor frame reunion class, adding a fourth small target detection layer, CBAM attention mechanism, SIoU positioning Loss function and Varifocal Loss classification loss function, and combining and comparing each algorithm to select the most suitable combination algorithm to solve the problem of ship target detection in the actual scenario. The original YOLOv7 network and the improved YOLOv7 network were used for experimental comparison on the self-built data set of inland waterway ships. Compared with the original network, the missing phenomenon of the improved YOLOv7 network model was greatly reduced, and the mAP of the improved YOLOv7 network model reached 90.6%, which increased by 13.7% compared with the original network model. The detection effect is better than the original network.","PeriodicalId":391180,"journal":{"name":"Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things","volume":"299302 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":"123444109","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}

引用次数: 0

LRFAN: a multi-scale large receptive field attention neural network LRFAN:一个多尺度大感受野注意神经网络

Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things Pub Date : 2023-05-26 DOI: 10.1145/3603781.3603834

Ci Song, Zhao Zhang

{"title":"LRFAN: a multi-scale large receptive field attention neural network","authors":"Ci Song, Zhao Zhang","doi":"10.1145/3603781.3603834","DOIUrl":"https://doi.org/10.1145/3603781.3603834","url":null,"abstract":"Transformer, which was originally used as a natural language processor, has rapidly gained importance in the field of computer vision since the introduction of ViT. The more efficient Transformer model challenges the dominance of convolution neural networks. In order to capture long-range dependencies, some convolution models have obtained performance gains by convolving very large kernels. However, as the size of convolution kernels grows, the computational complexity grows on the one hand, while speed begins to saturate on the other. In this paper, we propose a multi-scale large receptive field attention module (LRFA) that extracts feature information at different scales by grouping and superimposing different numbers of small-size convolutions. On the other hand, the superposition can have the effect of large kernel convolution, which reduces the computational complexity. LRFA overcomes the inability of conventional convolution neural networks to capture long-range dependencies and the inability of self-attention models to account for local feature information. We design an LRFA-based neural network, a multi-scale large receptive field attention neural network (LRFAN), which adjusts the superimposed convolution kernels size based on network depth and input feature information, and can adapt to the input feature maps to better capture long-range dependencies. Extensive experiments demonstrate that we outperform the conventional convolution neural network and the visual Transformer model in computer vision tasks such as image classification and object detection.","PeriodicalId":391180,"journal":{"name":"Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things","volume":"24 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":"121910987","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}

引用次数: 0

Real-Time Face Recognition with Mask using Deep Convolutional Neural Network

Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things Pub Date : 2023-05-26 DOI: 10.1145/3603781.3603863

Md. Ashif Mahmud Joy, Md. Fuad Hasan Khan Chowdhury, Sinha Afroz, Md. Nurul Islam, Ruaida Muhsinat, Mukta Akanda Moly, D. Farid

引用次数: 0

A Novel Elasticsearch Encryption Scheme for Intelligent Transportation System Applications 一种适用于智能交通系统的Elasticsearch加密方案

Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things Pub Date : 2023-05-26 DOI: 10.1145/3603781.3603874

Tao Wang, Haitao Huang, Ting Tian, Zhengda Zhou

引用次数: 0