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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

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visualization for Potential Pass Courses and Quantification for Offensive and Defensive Players in Basketball 7

Visualization for Potential Pass Courses and Quantification ...

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7th International conference on Engineering and Emerging Technologies (ICEET)

作者： Sano, Yusuke Nakada, Yohei Meiji Univ Grad Sch Adv Math Sci Tokyo Japan

ISBN: (纸本)9781665427142

Recently, data analysis and visualization of player and ball position data have attracted attention in basketball. Based on this, we previously proposed a method for predicting potential pass receivers in basketball, which can be utilized in a three-dimensional visualization tool to reproduce games. We propose three methods based on the previous prediction method for potential pass receivers. The first method is used to display representative pass courses for potential pass receivers. The second is used to evaluate potential pass receivers' relation to scores. The third method quantifies information regarding defensive players. The results produced by these three methods can be used to enhance users' understanding of attack patterns and plays in the visualization tool. These methods were also validated using real women's basketball datasets. As a result, it was demonstrated that the proposed methods performed effectively.

关键词： player and ball positions data basketball pass play prediction quantification of players sports data visualization

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Towards Cross Reality: A Comparison of World-in-Miniature and Virtual Reality Switch

Towards Cross Reality: A Comparison of World-in-Miniature an...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee conference on

作者： Damla Welti Mathieu Lutfallah Long Cheng Andreas Kunz ETH Zurich ETH Zurich & RhySearch

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

visualization of complex architectural and interior design proposals demands spatial comprehension. Mixed Reality (MR) has opened new ways for architectural visualization through its ability to overlay virtual models onto already existing real environments. However, this approach alone restricts users to the physical constraints of their surroundings. We developed two approaches to augment MR with additional perspectives from physically inaccessible viewpoints: the World-in-Miniature (WIM) solution and the Cross Reality (CR) solution. The WIM solution provides a scaled-down 3D replica of the virtual environment, whereas the CR solution enables users to switch to Virtual Reality and teleport to physically inaccessible viewpoints. Our research aims to explore whether one solution outperforms the other.

关键词： visualization Solid modeling Three-dimensional displays Mixed reality Virtual environments Prototypes Virtual reality Switches User interfaces Software

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Data visualization and Machine Learning Approach for Analyzing Severity of Road Accidents

Data Visualization and Machine Learning Approach for Analyzi...

引用

2022 International conference for Advancement in Technology, ICONAT 2022

作者： Vijithasena, Rasika Herath, Wikasitha University of Moratuwa Department of Information Technology Sri Lanka University of Moratuwa Department of Computer Science Engineering Sri Lanka

ISBN: (纸本)9781665425773

Road accidents are a major critical problem in recent days, which cause death, severe injuries, disabilities to human lives, and substantial economic losses worldwide. A detailed analysis of the factors responsible for the accidents is required to reduce the accident rate. In this study, a descriptive analysis has been performed in-Depth to identify the significant factors influencing accident severity. Machine learning techniques have been applied to predict the severity of accidents considering location, time, infrastructure, and environmental conditions-related factors that may cause road accidents. Here the findings of this study emphasize that the mediate severity of accidents have a high frequency of occurring rather than severities of very low and high risk. Moreover, the factors like infrastructure, day of the week, and weather conditions influence the severity of accidents differently. The Random Forest algorithm took the best performance with 97.2% high accuracy in predicting the severity of road traffic accidents. These results can guide relevant parties to identify dangerous situations and take the necessary actions to improve road safety by reducing accidents. © 2022 ieee.

关键词： Data visualization

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SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimation

SAR-Net: Shape Alignment and Recovery Network for Category-l...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lin, Haitao Liu, Zichang Cheang, Chilam Fu, Yanwei Guo, Guodong Xue, Xiangyang Fudan Univ Shanghai Peoples R China Baidu Res IDL Beijing Peoples R China Acad Engn & Technol Shanghai Peoples R China Engn Res Ctr AI & Robot Shanghai Peoples R China Fudan Univ Sch Data Sci Shanghai Peoples R China Zhejiang Normal Univ Fudan ISTBI ZJNU Algorithm Ctr Brain Inspired Int Jinhua Zhejiang Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Given a single scene image, this paper proposes a method of Category-level 6D Object Pose and Size Estimation (COPSE) from the point cloud of the target object, without external real pose-annotated training data. Specifically, beyond the visual cues in RGB images, we rely on the shape information predominately from the depth (D) channel. The key idea is to explore the shape alignment of each instance against its corresponding category-level template shape, and the symmetric correspondence of each object category for estimating a coarse 3D object shape. Our framework deforms the point cloud of the category-level template shape to align the observed instance point cloud for implicitly representing its 3D rotation. Then we model the symmetric correspondence by predicting symmetric point cloud from the partially observed point cloud. The concatenation of the observed point cloud and symmetric one reconstructs a coarse object shape, thus facilitating object center (3D translation) and 3D size estimation. Extensive experiments on the category-level NOCS benchmark demonstrate that our lightweight model still competes with state-of-the-art approaches that require labeled real-world images. We also deploy our approach to a physical Baxter robot to perform grasping tasks on unseen but category-known instances, and the results further validate the efficacy of our proposed model. Code and pre-trained models are available on the project webpage(1).

关键词： Point cloud compression visualization Three-dimensional displays Shape Estimation Training data Predictive models

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A Service-based information System for AI-supported Health Informatics 5

A Service-based Information System for AI-supported Health I...

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5th ieee International conference on Big Data and Artificial Intelligence (BDAI)

作者： Reis, Thoralf Steffl, Andreas Bruchhaus, Sebastian Freund, Florian Bornschlegl, Marco X. Hemmje, Matthias L. Univ Hagen Fac Math & Comp Sci Hagen Germany

ISBN: (数字)9781665470810

ISBN: (纸本)9781665470810

Artificial Intelligence (AI) and Big Data Analysis are both trending research areas that can be applied to any application domain to increase efficiency, quality, or bridge supply shortages. An example is the analysis of medical information in health informatics and its subdomains bioinformatics and medical informatics. It aims to explore medical information like, e.g., patient data or clinical studies in order to support medical doctors, healthcare professionals, and ultimately all patients. The increasing level of digitization of people's lives and global pandemics boost the relevance of this research area even further. This paper analyzes a medical information use case in order to extend and improve an existing information System for AI-based Visual Big Data Analysis with a special focus on user empowerment. In more detail, this paper extends a software architecture, introduces a reference implementation, and refines the underlying reference model AI2VIS4BigData. It focuses on supporting both, expert users with profound technical knowledge and end users without that expertise. The resulting concepts and implementations are consequently evaluated with both target groups: A cognitive walkthrough with a group of AI and Big Data Analysis experts confirms the system's usability and a survey with medical doctors and health domain experts (potential end users) positively evaluates the developed concepts.

关键词： information systems electronic healthcare Big Data applications artificial intelligence data visualization

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Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency

Learning from Untrimmed Videos: Self-Supervised Video Repres...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Qing, Zhiwu Zhang, Shiwei Huang, Ziyuan Xu, Yi Wang, Xiang Tang, Mingqian Gao, Changxin Jin, Rong Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Alibaba Grp Hangzhou Peoples R China Natl Univ Singapore ARC Singapore Singapore Dalian Univ Technol Dalian Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Natural videos provide rich visual contents for selfsupervised learning. Yet most existing approaches for learning spatio-temporal representations rely on manually trimmed videos, leading to limited diversity in visual patterns and limited performance gain. In this work, we aim to learn representations by leveraging more abundant information in untrimmed videos. To this end, we propose to learn a hierarchy of consistencies in videos, i.e., visual consistency and topical consistency, corresponding respectively to clip pairs that tend to be visually similar when separated by a short time span and share similar topics when separated by a long time span. Specifically, a hierarchical consistency learning framework HiCo is presented, where the visually consistent pairs are encouraged to have the same representation through contrastive learning, while the topically consistent pairs are coupled through a topical classifier that distinguishes whether they are topicrelated. Further, we impose a gradual sampling algorithm for proposed hierarchical consistency learning, and demonstrate its theoretical superiority. Empirically, we show that not only HiCo can generate stronger representations on untrimmed videos, it also improves the representation quality when applied to trimmed videos. This is in contrast to standard contrastive learning that fails to learn appropriate representations from untrimmed videos.

关键词： Representation learning visualization Computer vision Supervised learning Performance gain Pattern recognition Classification algorithms

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Direction-Aware Adaptive Online Neural Speech Enhancement with an Augmented Reality Headset in Real Noisy Conversational Environments

Direction-Aware Adaptive Online Neural Speech Enhancement wi...

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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Sekiguchi, Kouhei Nugraha, Aditya Arie Du, Yicheng Bando, Yoshiaki Fontaine, Mathieu Yoshii, Kazuyoshi RIKEN Ctr Adv Intelligence Project AIP Tokyo Japan Kyoto Univ Grad Sch Informat Kyoto Japan Natl Inst Adv Ind Sci & Technol Tokyo Japan Inst Polytech Paris LTCI Telecom Paris Paris France

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

This paper describes the practical response- and performance-aware development of online speech enhancement for an augmented reality (AR) headset that helps a user understand conversations made in real noisy echoic environments (e.g., cocktail party). One may use a state-of-the-art blind source separation method called fast multichannel nonnegative matrix factorization (FastMNMF) that works well in various environments thanks to its unsupervised nature. Its heavy computational cost, however, prevents its application to real-time processing. In contrast, a supervised beamforming method that uses a deep neural network (DNN) for estimating spatial information of speech and noise readily fits real-time processing, but suffers from drastic performance degradation in mismatched conditions. Given such complementary characteristics, we propose a dual-process robust online speech enhancement method based on DNN-based beamforming with FastMNMF-guided adaptation. FastMNMF (back end) is performed in a mini-batch style and the noisy and enhanced speech pairs are used together with the original parallel training data for updating the direction-aware DNN (front end) with backpropagation at a computationally-allowable interval. This method is used with a blind dereverberation method called weighted prediction error (WPE) for transcribing the noisy reverberant speech of a speaker, which can be detected from video or selected by a user's hand gesture or eye gaze, in a streaming manner and spatially showing the transcriptions with an AR technique. Our experiment showed that the word error rate was improved by more than 10 points with the runtime adaptation using only twelve minutes observation.

关键词： Headphones visualization Sensitivity Array signal processing Training data Speech enhancement Streaming media

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Challenges for Driver Action Recognition with Face Masks 25

Challenges for Driver Action Recognition with Face Masks

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ieee 25th International conference on Intelligent Transportation Systems (ITSC)

作者： Amparore, Elvio G. Botta, Marco Drago, Idilio Donatelli, Susanna Mazzone, Giuseppe Univ Torino Turin Italy

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Advanced Driver Assistance Systems (ADAS) are enabling technologies in Intelligent Transportation Systems. Modern ADAS include algorithms to classify drivers' actions and distractions, aiming at identifying situations in which the driver is inattentive. Such systems typically include components for Driver Action Recognition (DAR) and Visual Distraction Classification (VDC), which prevent risky situations during semi-autonomous driving. DAR and VDC often rely on cameras that track the driver and classify actions based on image recognition algorithms. The COVID-19 pandemic has changed several common social behaviours, including the widespread use of face mask even during driving. In some cases (taxi, bus) face covering policies are compulsory in many legislations. We here show that these behavioural changes challenge state-of-theart DAR and VDC systems, with the average F1-score in some scenarios dropping by around 30% when exposed to images of drivers wearing masks. Noting a lack of public datasets to update the ML classifiers performing such tasks, we contribute MASKDAR, a dataset for Action Recognition of Drivers wearing face Masks. Finally, using MASKDAR we show the importance of including subjects with face masks in datasets for DAR.

关键词： visualization Image recognition Pandemics Face recognition Legislation Classification algorithms Task analysis

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Development of an Interactive Map Within the Implementation of Actual State and Public Directions 12

Development of an Interactive Map Within the Implementation ...

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12th International conference on Advanced Computer information Technologies, ACIT 2022

作者： Lupei, Maksym Shlahta, Myroslav Mitsa, Oleksandr Horoshko, Yurii Tsybko, Hanna Gorbachuk, Vasyl Institute of Cybernetics of National Academy of Sciences of Ukraine Department of Intellectual Information Technologies Kyiv Ukraine Lviv National University Department of Ukrainian Geographics Lviv Ukraine Uzhhorod National University Department of Informative and Operating Systems and Technologies Uzhhorod Ukraine Taras Shevchenko National University 'Chernihiv Colehium' Department of Computer Science and Computer Engineering Chernihiv Ukraine

ISBN: (数字)9781665410502

ISBN: (纸本)9781665466479

The problem of developing an effective way to display selected dynamic information of geographical content on an interactive map is being investigated. Various approaches to solving this problem are analyzed, including the use of Python programming language. Pointing out their advantages and disadvantages, the paper proposes an approach that allows one to complete the task in full with high efficiency and low cost of time and human resources. It is based on a model that uses a Data Science methodology of five stages: obtaining primary data, data processing, necessary calculations, visualization creating and visualization disseminating. As a source of data there were used open data from the web portal 'Diia', developed by the Ministry of Digital Transformation of Ukraine, and data from the Institute of National Memory. The processing of the data was as follows: each toponym was assigned a localization binding and it was determined whether it falls under the law on decommunization or not. The processed data were visualized using the Google Maps web mapping service. Each non-decommunized object was marked on the map. The interactive map is integrated into the Analitycs-UA web portal. © 2022 ieee.

关键词： Geographic information systems

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Implementation and visualization of Path Finding Algorithms 5

Implementation and Visualization of Path Finding Algorithms

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5th International conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Bhatt, Chandradeep Sharma, Ritabh Chauhan, Rahul Vishvakarma, Ashish Manchanda, Mahesh Sharma, Sanjay Graphic Era Hill University Cse Department Dehradun India Graphic Era Hill University Computer Science and Engineering Dehradun India Institute of Technology & Management Department of Information Technology Uttarakhand India Sgrrits Computer Application Department Dehradun India

ISBN: (纸本)9798350321425

Learning computer science is tough for many students, especially the ones who don't have any knowledge about it previously. Just like other subjects, there are a lot of things that needs to be learned in Computer Science. It is often to seen that students find it difficult to cope up with the extreme change in their learning environment. There are many reasons why students struggle in this field. 'Algorithms' is one such topic that students find the most confusing. Lecturers on the other hand, also face problems in describing the algorithms to students. Based on several researches, Algorithm visualization has been to have a positive impact on learning abilities of student. It is already known that a person can learn much more efficiently if it sees what is going on and how is it done. Pathfinding refers to the exploration of an optimal and shortest route, commencing from a designated initial point and concluding at a specified destination. Within the realm of artificial intelligence, pathfinding algorithms are specifically crafted to navigate through graph structures. These algorithms find utility in diverse domains like navigation systems, computer gaming, network architectures, and beyond. Their performance can be degraded by numerous factors such as the length of the path, size of the problem, obstacles and its distribution, data structures used and much more. Whenever a new path-finding algorithm is suggested, its performance is often investigated on the basis of the observations made when that algorithm is used. Proper design and analysis play an important role in giving an explanatory and precise evaluation. By conducting analysis of multiple research works, this study systematically categorized them based on their provisional designs, methodologies, and logical approaches. Throughout this process, this work discovered minor imperfections in these algorithms, which were frequently observed as well. © 2023 ieee.

关键词： Students

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