2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)最新文献_第4页

Centralized Motion-Aware Enhancement for Single Object Tracking on Point Clouds 点云上单目标跟踪的集中运动感知增强

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016372

Yue Wu, Jiaming Liu, Maoguo Gong, Wenping Ma, Q. Miao

{"title":"Centralized Motion-Aware Enhancement for Single Object Tracking on Point Clouds","authors":"Yue Wu, Jiaming Liu, Maoguo Gong, Wenping Ma, Q. Miao","doi":"10.1109/CCIS57298.2022.10016372","DOIUrl":"https://doi.org/10.1109/CCIS57298.2022.10016372","url":null,"abstract":"3D Single Object Tracking (SOT) in LiDAR point clouds has broad application prospects in computer vision, and objects are usually represented by 3D boxes in point clouds. Current methods mostly follow the representation matching-based siamese pattern. However, due to the severe sparse, incomplete shapes of LiDAR point clouds, and the fact that objects in the 3D world do not follow any specific orientation, these are common obstacles to point cloud tracking. In this paper, we propose to represent 3D objects as points, using a key point detector to detect the center of the object and enhance the feature description of the target object, based on a simple and efficient way for more accurate feature comparison. In particular, we introduce a motion-centric paradigm that localizes objects via motion in successive frame transformations. Experimental results demonstrate that our proposed method achieves satisfactory results on both the KITTI and nuScenes benchmarks, achieving a ~ 10% improvement in accuracy compared to state-of-the-art methods. Furthermore, our analysis confirms the effectiveness of each component and shows the great potential of the motion-centric paradigm when combined with representation matching.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128842836","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: 1

Face Image Smart Capture System Using Embedded Device 基于嵌入式设备的人脸图像智能采集系统

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016343

Zhaoxia Wang, Yongxin Liu

{"title":"Face Image Smart Capture System Using Embedded Device","authors":"Zhaoxia Wang, Yongxin Liu","doi":"10.1109/ccis57298.2022.10016343","DOIUrl":"https://doi.org/10.1109/ccis57298.2022.10016343","url":null,"abstract":"At present, as an important means of identity authentication, face image recognition has been widely used in many fields. However, during obtaining a face image, it is often necessary for the person being captured to adjust his positions of face or a staff help to manually adjust the cameras position repeatedly. With this, the face images captured may meet the authentication requirements. For solving these problems, an intelligent face image capture system based on embedded device is designed. The hardware mainly consists of a control module with STM32F103C8T6 as CPU, and a two-degree-of-freedom pan tilt zoom camera (PTZ) with two steering gears and a digital camera. The two-scale fitting inverse combination active appearance model (AAM) algorithm is used for face image detection and location. Then the steering gear is controlled to adjust the camera’s direction according to the relationship between the center of the face image and the center of the camera’s field of view (FOV) to complete the face image capture automatically. Experiments show that the designed system meets the authentication requirements and has the good real-time performance and stability.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129276759","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

Dialogue-enriched Knowledge Point Recommendation for Consultation Task 对话丰富的咨询任务知识点建议

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016340

Zheyong Xie, Shiwei Wu, Jia Su, Tong Xu, Enhong Chen

引用次数: 0

Design and Implementation of Standardized Universal P2P Protocol Framework Based on Central Server 基于中心服务器的标准化通用P2P协议框架的设计与实现

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016346

Shuo Han, Yuan Wen, Tao Sun, Haoyuan Wang, Wen‐Tao Wu

引用次数: 0

Multi-UAV Formation Control Based on Parameter Optimization ADRC 基于参数优化自抗扰控制器的多无人机编队控制

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016414

Yuxin Hu, Changlin Liu, Ping Wang, Mengping Zhang, Han Mu, Quan Yuan

引用次数: 1

Research on the Application of Big Data in the Field of Aviation Safety 大数据在航空安全领域的应用研究

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016345

Dawei Li, B. Ren, Jianguo Gao, Jihui Xu

{"title":"Research on the Application of Big Data in the Field of Aviation Safety","authors":"Dawei Li, B. Ren, Jianguo Gao, Jihui Xu","doi":"10.1109/ccis57298.2022.10016345","DOIUrl":"https://doi.org/10.1109/ccis57298.2022.10016345","url":null,"abstract":"At present, big data has shown great application effect and broad development prospect in many industries. This paper summarizes main applications of big data in the field of aviation safety, and analyzes main scenarios and difficulties that may be faced in further application of big data in the field of aviation safety, including data size and scale, format, error and normalization. In view of the sparse and high-dimensional characteristics of observable variables in aviation safety big data analysis, the L1/2 regularization method is introduced into data dimensionality reduction. A variable selection algorithm is designed and verified by a case. Simulation results show that the algorithm designed can mine effective variables with high correlation to aviation safety result. Suggestions for development of big data in the field of aviation safety are put forward from the aspects of institutional mechanism construction, basic theory research, professional personnel training, database construction, etc., which has certain reference value for application and development of big data in the field of aviation safety.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131773145","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

Remote Sensing Image River Segmentation Method Based on U-Net 基于U-Net的遥感图像河流分割方法

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016397

Qiang Cai, Ruyi Wan, Haisheng Li, Chen Wang, Haodong Chang

{"title":"Remote Sensing Image River Segmentation Method Based on U-Net","authors":"Qiang Cai, Ruyi Wan, Haisheng Li, Chen Wang, Haodong Chang","doi":"10.1109/CCIS57298.2022.10016397","DOIUrl":"https://doi.org/10.1109/CCIS57298.2022.10016397","url":null,"abstract":"River segmentation based on remote sensing images plays an important role in water conservancy business work, water wading monitoring work, and flood disaster prevention. In actual remote sensing images of rivers, most of the backgrounds are complex, and there is no public remote sensing image dataset specifically for the study of river segmentation. The traditional river segmentation methods have rough edge information and serious noise. To solve the above problems, this paper firstly preprocesses the Gaofen Image Dataset (GID) and Remote Sensing Image Block Segmentation Dataset (BDCI), and creates two datasets for river segmentation in high-resolution remote sensing images respectively (GID-river and BDCI-river) and then proposed a river segmentation method based on U-Net. On the basis of the original U-Net, the ResNet34 and VGG16 structures were combined to strengthen the feature extraction ability of the network, so as to achieve more accurate river edge details. The experimental results shows the mIoU of the ResNet34-UNet network on the GID-river dataset reaches 93.6%, and the mPA of the VGG16-UNet network on the BDCI-river dataset reaches 82.1%.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130365376","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

Securing QR Codes Using a Hybrid Pseudo Baker’s Mapped Cellular Automation 使用混合伪贝克映射细胞自动化保护QR码

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016396

Abdullah M. Iliyasu, Abubakar M. Iliyasu

{"title":"Securing QR Codes Using a Hybrid Pseudo Baker’s Mapped Cellular Automation","authors":"Abdullah M. Iliyasu, Abubakar M. Iliyasu","doi":"10.1109/CCIS57298.2022.10016396","DOIUrl":"https://doi.org/10.1109/CCIS57298.2022.10016396","url":null,"abstract":"Since their advent in 1994, Quick Response (QR) codes have grown to become ubiquitous in wide-ranging applications covering marketing, healthcare, commerce, and many other areas. Increasingly, commerce, QR codes are taking up roles where confidentiality, privacy, and integrity of the information they transmit or and/or the identities of parties involved in the communication is vital. Meanwhile, cellular automation (CA) is veritable tool for abstract dynamical information processing with finite number of discrete states space, and time that can be updated synchronously contents and is steered. By employing a pseudo baker’s map, we design a template to scramble the QR code content whose evolution is steered using the dextral boundary condition (DBC) of cellular automation. The DBC combines a group of cells permeating contents of a QR code tile at state t to determine the left-most cell entry at state t+1. We evaluated our protocol by implementing both the encryption and recovery procedures on various QR codes, and our findings show that, our method can retain the physical appearance and heterogeneity of the QR code during both the encryption and decryption processes. This is manifest in 100% concordance between the original and recovered QR codes, which is better than what was reported in similar schemes.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115067597","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

Research on Denoising of Multiple Compressed Image Based on Structure-Texture Decomposition 基于结构-纹理分解的多幅压缩图像去噪研究

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016428

Faguo Zhou, Qiqi Liu, X. Wang

{"title":"Research on Denoising of Multiple Compressed Image Based on Structure-Texture Decomposition","authors":"Faguo Zhou, Qiqi Liu, X. Wang","doi":"10.1109/ccis57298.2022.10016428","DOIUrl":"https://doi.org/10.1109/ccis57298.2022.10016428","url":null,"abstract":"At present, most of the researches on denoising of compressed images are one-time compression. But in practice, the image will be compressed more than once. Therefore, a denoising method combining deep learning and traditional methods was proposed for multiple compressed images. First, the data set was compressed twice through singular value decomposition (SVD) to obtain noisy images after multiple compressions. Secondly, the noisy image was decomposed to obtain the noisy structure image and texture image. Then denoised them separately, the noisy structure image was used the feed-forward denoising convolutional neural network (DnCNN), and the noisy texture image was used the selected mean method. Finally, the denoised structure image and texture image were combined to obtain the denoised multiple compressed image. The Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) of this method are approximately improved by 0.9~1.5dB and 0.02~0.06, respectively. Moreover, the texture image is extracted and targeted for denoising, retaining more detailed information and achieving a clearer visual effect.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131238349","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

Research on scheduling path planning of multi-objective unmanned tractor based on reinforcement learning method 基于强化学习方法的多目标无人驾驶拖拉机调度路径规划研究

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016314

Haichen Wang, Huarui Wu, Ning Zhang

引用次数: 0