PassVizor: Toward Better Understanding of the Dynamics of Soccer Passes

Xie, Xiao; Wang, Jiachen; Liang, Hongye; Deng, Dazhen; Cheng, Shoubin; Zhang, Hui; Chen, Wei; Wu, Yingcai

doi:10.1109/tvcg.2020.3030359

Cited by 53 publications

(15 citation statements)

References 34 publications

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“…The dynamic node deployment problem has two main dynamic characteristics; on the one hand, if the network is joined with movable sensor nodes, when the redeployment demand occurs in the network, the deployment algorithm control the relevant nodes to move to the best deployment location autonomously to optimize the network structure, and the network nodes remain stationary after redeployment; on the other hand, for dynamic wireless sensor networks in which the nodes are all movable. The deployment algorithm will move and schedule the nodes appropriately according to the monitoring needs of the target area [21]. The strategy of joining dynamic multi-intelligent body robot formations is usually applied to centralized algorithms in dynamic network deployment optimization problems and does not apply to ordinary sensor networks where nodes have limited computational power.…”

Section: Experimental Design For Optimizing Sports Eventmentioning

confidence: 99%

Digitalization and Information Management Mechanism of Sports Events Based on Cooperative Sensing Model of Multisensor Nodes

2022

Journal of Sensors

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This paper provides an in-depth study and analysis of the optimization of sports event management systems using wireless sensor networks. Aiming at the monitoring task of a directed wireless sensor network in a three-dimensional environment, the directed sensing nodes scattered inside the designated monitoring area in a random deployment manner usually have uneven distribution and other problems; we analyze the characteristics of the directed sensor nodes, probabilistic sensing model, and the cooperative sensing model of multiple sensor nodes for monitoring target points and propose a sensing optimization strategy in polar coordinates to guide the three-dimensional plane directed orientation adjustment and sensing optimization of sensor nodes, thus enhancing the sensing capability of network nodes. The experimental results confirm that the algorithm can improve the coverage of the area to be monitored and the quality of sensing service, and it reduces the overall energy consumption of the network by using the distributed node synchronization scheduling mechanism to extend the life cycle of the network to maintain good monitoring capability under the premise of the limited total usage of the directed nodes in wireless sensor networks. The application of wireless sensor network technology in sports competition management mainly includes the application of smart wearable devices in sports competition training, the application of goal-line technology in sports competition, and the application of eagle eye technology in sports events, all three technologies have certain advantages in the application of sports competition, and all of their help to promote the improvement of sports event management and the development of sports industry; the second aspect is wireless sensor. The second aspect is the application of wireless sensor network technology in sports event information management, which is mainly used to collect information related to sports events and fully utilize it to make sports event management more informative and digital, which is helpful to improve the level of sports event management; the third aspect is the application of wireless sensor network technology in sports event stadium management, which is mainly based on intelligent stadiums to create a more spectator-friendly and good experience for the audience, a more ornamental and good experience viewing place, to promote the development and growth of sports industry.

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Section: Experimental Design For Optimizing Sports Eventmentioning

confidence: 99%

Digitalization and Information Management Mechanism of Sports Events Based on Cooperative Sensing Model of Multisensor Nodes

2022

Journal of Sensors

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“…A study using video images of soccer visualized analyses such as team tactics and player evaluation ( Sheng et al, 2020 ). In a study based on pass data from soccer games, the changing patterns of tactics were visualized by comprehensively analyzing pass data, including the related dynamics ( Xie et al, 2020 ).…”

Section: Related Workmentioning

confidence: 99%

DNN-based multi-output model for predicting soccer team tactics

Lee¹,

Jung²

2022

PeerJ Computer Science

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In modern sports, strategy and tactics are important in determining the game outcome. However, many coaches still base their game tactics on experience and intuition. The aim of this study is to predict tactics such as formations, game styles, and game outcome based on soccer dataset. In this paper, we propose to use Deep Neural Networks (DNN) based on Multi-Layer Perceptron (MLP) and feature engineering to predict the soccer tactics of teams. Previous works adopt simple machine learning techniques, such as Support Vector Machine (SVM) and decision tree, to analyze soccer dataset. However, these often have limitations in predicting tactics using soccer dataset. In this study, we use feature selection, clustering techniques for the segmented positions and Multi-Output model for Soccer (MOS) based on DNN, wide inputs and residual connections. Feature selection selects important features among features of soccer player dataset. Each position is segmented by applying clustering to the selected features. The segmented positions and game appearance dataset are used as training dataset for the proposed model. Our model predicts the core of soccer tactics: formation, game style and game outcome. And, we use wide inputs and embedding layers to learn sparse, specific rules of soccer dataset, and use residual connections to learn additional information. MLP layers help the model to generalize features of soccer dataset. Experimental results demonstrate the superiority of the proposed model, which obtain significant improvements comparing to baseline models.

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“…Video visualization has been investigated in the fields of multimedia analysis [13,34,75] and information visualization [7,16,42,57,64,65,69,71,72] for decades. We consider the relevant studies on visual video summarization and visual video analytics that are associated with video reviewing.…”

Section: Video Visualizationmentioning

confidence: 99%

VideoModerator: A Risk-aware Framework for Multimodal Video Moderation in E-Commerce

Tang¹,

Wu²,

Yu³

et al. 2021

Preprint

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Video moderation, which refers to remove deviant or explicit content from e-commerce livestreams, has become prevalent owing to social and engaging features. However, this task is tedious and time consuming due to the difficulties associated with watching and reviewing multimodal video content, including video frames and audio clips. To ensure effective video moderation, we propose VideoModerator, a risk-aware framework that seamlessly integrates human knowledge with machine insights. This framework incorporates a set of advanced machine learning models to extract the risk-aware features from multimodal video content and discover potentially deviant videos. Moreover, this framework introduces an interactive visualization interface with three views, namely, a video view, a frame view, and an audio view. In the video view, we adopt a segmented timeline and highlight high-risk periods that may contain deviant information. In the frame view, we present a novel visual summarization method that combines risk-aware features and video context to enable quick video navigation. In the audio view, we employ a storyline-based design to provide a multi-faceted overview which can be used to explore audio content. Furthermore, we report the usage of VideoModerator through a case scenario and conduct experiments and a controlled user study to validate its effectiveness.

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PassVizor: Toward Better Understanding of the Dynamics of Soccer Passes

Cited by 53 publications

References 34 publications

Digitalization and Information Management Mechanism of Sports Events Based on Cooperative Sensing Model of Multisensor Nodes

Digitalization and Information Management Mechanism of Sports Events Based on Cooperative Sensing Model of Multisensor Nodes

DNN-based multi-output model for predicting soccer team tactics

VideoModerator: A Risk-aware Framework for Multimodal Video Moderation in E-Commerce

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