ShuttleSpace: Exploring and Analyzing Movement Trajectory in Immersive Visualization

Ye, Shuainan; Chen, Zhutian; Chu, Xiangtong; Wang, Yifan; Fu, Siwei; Shen, Lejun; Zhou, Kun; Wu, Yingcai

doi:10.1109/tvcg.2020.3030392

Cited by 66 publications

(29 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Researchers have proposed a set of visualization techniques and tools to analyze different sports data, such as the basketball [5,6,21], the baseball [23], and racquet sports [32][33][34]37]. Specifically, a number of approaches have been introduced to present and analyze soccer data from different aspects.…”

Section: Visualization Of Soccermentioning

confidence: 99%

PassVizor: Toward Better Understanding of the Dynamics of Soccer Passes

Xie

Wang

Liang

et al. 2021

IEEE Trans. Visual. Comput. Graphics

Self Cite

View full text Add to dashboard Cite

In soccer, passing is the most frequent interaction between players and plays a significant role in creating scoring chances. Experts are interested in analyzing players passing behavior to learn passing tactics, i.e., how players build up an attack with passing. Various approaches have been proposed to facilitate the analysis of passing tactics. However, the dynamic changes of a teams employed tactics over a match have not been comprehensively investigated. To address the problem, we closely collaborate with domain experts and characterize requirements to analyze the dynamic changes of a teams passing tactics. To characterize the passing tactic employed for each attack, we propose a topic-based approach that provides a high-level abstraction of complex passing behaviors. Based on the model, we propose a glyph-based design to reveal the multi-variate information of passing tactics within different phases of attacks, including player identity, spatial context, and formation. We further design and develop PassVizor, a visual analytics system, to support the comprehensive analysis of passing dynamics. With the system, users can detect the changing patterns of passing tactics and examine the detailed passing process for evaluating passing tactics. We invite experts to conduct analysis with PassVizor and demonstrate the usability of the system through an expert interview.

show abstract

Section: Visualization Of Soccermentioning

confidence: 99%

PassVizor: Toward Better Understanding of the Dynamics of Soccer Passes

Xie

Wang

Liang

et al. 2021

IEEE Trans. Visual. Comput. Graphics

Self Cite

View full text Add to dashboard Cite

show abstract

“…[36] . ShuttleSpace [4] [32] . 这使得交互空间不再限于手臂范围之内 [41] , 也降低了用户选择远距离数据所 [57] 和城市场景可视化 [16] , 也有针对抽象数据的感知与分析, 如关系网络 [12] 和散点图 [18] .…”

Section: 三维视觉感知unclassified

A Survey on Immersive Visualization

Chu

2021

J Comput-Aided Design Comput Graphics

Self Cite

View full text Add to dashboard Cite

Immersive visualization is an emerging research field. It aims to make full use of human perception and operation capabilities through the immersion and engagement offered by the immersive interface to support data analysis, reasoning, and decision-making. The latest research on immersive visualization is reviewed. Firstly, the design and evaluation of the visual and non-visual perception in information visualization are introduced. Secondly, the newest interaction methods from the aspects of touch, gesture, gaze, and navigation are summarized. Afterward, the visualization authoring toolkit in terms of desktop, mobile, and mix terminals is overviewed. Finally, the industry trends, applications, and challenges of immersive visualization are discussed.

show abstract

“…ShuttleSpace [27] was developed by Ye et al to help badminton experts in evaluating trajectory data. Sports trajectory data, such as the movement of players and balls, has a wealth of information on player behavior.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid design for sports data visualization using AI and big data analytics

Liu

Mahapatra

Mayuri

2021

Complex Intell. Syst.

View full text Add to dashboard Cite

In sports data analysis and visualization, understanding collective tactical behavior has become an integral part. Interactive and automatic data analysis is instrumental in making use of growing amounts of compound information. In professional team sports, gathering and analyzing sportsperson monitoring data are common practice, intending to evaluate fatigue and succeeding adaptation responses, analyze performance potential, and reduce injury and illness risk. Data visualization technology born in the era of big data analytics provides a good foundation for further developing fitness tools based on artificial intelligence (AI). Hence, this study proposed a video-based effective visualization framework (VEVF) based on artificial intelligence and big data analytics. This study uses the machine learning method to categorize the sports video by extracting both the videos' temporal and spatial features. Our system is based on convolutional neural networks united with temporal pooling layers. The experimental outcomes demonstrate that the recommended VEVF model enhances the accuracy ratio of 98.7%, recall ratio of 94.5%, F1-score ratio of 97.9%, the precision ratio of 96.7%, the error rate of 29.1%, the performance ratio of 95.2%, an efficiency ratio of 96.1% compared to other existing models.

show abstract

ShuttleSpace: Exploring and Analyzing Movement Trajectory in Immersive Visualization

Cited by 66 publications

References 42 publications

PassVizor: Toward Better Understanding of the Dynamics of Soccer Passes

PassVizor: Toward Better Understanding of the Dynamics of Soccer Passes

A Survey on Immersive Visualization

Hybrid design for sports data visualization using AI and big data analytics

Contact Info

Product

Resources

About