To Start Automatic Commentary of Soccer Game with Mixed Spatial and Temporal Attention

Chan, Cheuk-Yiu; Hui, Chun-Chuen; Siu, Wan Chi; Chan, Sin-wai; Chan, H. Anthony

doi:10.1109/tencon55691.2022.9978078

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…A transformer (Temporal-Grouped): is then used to identify important features with our proposed mixed attention mechanism, which contains multiple sets of trainable parameters for each frame, that entertains the awareness of temporal information. A Position Encoding Generator (PEG) which contains one convolution step is then used for analyzing position information [3]. Subsequently, another transformer (Normal)network is used, which has the same structure as the previous one, whilst it groups all channels together with only 1 set of trainable parameters.…”

Section: Overall Architecturementioning

confidence: 99%

“…Commentators may not always be available, which becomes very desirable to have an automatic commentary system to carry out this work. Machine Learning has been found very useful in various signal processing tasks [1][2][3][4]; hence we make use of a mix of both deep learning and conventional signal/image processing techniques to effect automatic commentary. Even though there is not much in depth research on this topic, there are still researchers working on various parts related automatic commentary, say for example action spotting [2] in football games.…”

Section: Introductionmentioning

confidence: 99%

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On the completion of automatic football game commentary system with deep learning

Siu

Chan

et al. 2023

International Workshop on Advanced Imaging Technology (IWAIT) 2023

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Due to the recent development of machine learning, quality automatic football game commentary is getting possible. By now most of the effort of researchers has spent on key action spotting. This paper is on our strategy for filling in the gap between key actions in order to form a complete automatic football game commentary system. Novel deep learning and signal processing techniques have been used/created in this research work, and attractive demonstrations are available.

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Section: Overall Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the completion of automatic football game commentary system with deep learning

Siu

Chan

et al. 2023

International Workshop on Advanced Imaging Technology (IWAIT) 2023

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“…Therefore, the robots need to understand the game situations and behave accordingly. Although studies have generated automatic commentary for sports games based on the situation [20][21][22][23][24][25][26][27], the commentary is in the position of an indirect third party with respect to spectators, hindering the shared experience.…”

Section: Baseball Spectator Assistancementioning

confidence: 99%

Proposal and Evaluation of a Robot to Improve the Cognitive Abilities of Novice Baseball Spectators Using a Method for Selecting Utterances Based on the Game Situation

Mamada,

Miyamoto,

Katagami

2023

Applied Sciences

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Herein, an engineering method is developed to improve spectators’ sports-watching abilities. We present an interactive robot that supports the cognitive abilities of baseball novices in sports spectating. The robot watches a game with a person and utters words applicable to the game situation. We propose two methods to support cognitive watching: five categories of utterances (confirmation, information, prediction, emotion, and evaluation) and utterance rules for player participation and game scenes. We also propose a method for generating utterances for each category. To evaluate the generated utterances, we conducted an experiment whereby spectators watched baseball footage with the robot. The results of the experiment showed that the robot’s utterances could support the cognitive ability sub-factor of individual game intelligence, specifically “Spectating while knowing the player’s strengths.” In addition, the feeling of heaviness that occurs when watching alone could potentially be reduced by watching with a robot. This study is the first to attempt to support and improve spectators’ abilities to watch sports using the human–robot interaction approach. We anticipate that this approach will be used in the future to build a relationship (increase intimacy) with an agent (robot) and to support and improve cognitive abilities regardless of the type of sport.

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SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap

Somers,

Joos,

Cioppa

et al. 2024

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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To Start Automatic Commentary of Soccer Game with Mixed Spatial and Temporal Attention

Cited by 4 publications

References 27 publications

On the completion of automatic football game commentary system with deep learning

On the completion of automatic football game commentary system with deep learning

Proposal and Evaluation of a Robot to Improve the Cognitive Abilities of Novice Baseball Spectators Using a Method for Selecting Utterances Based on the Game Situation

SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap

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