TenniSet: A Dataset for Dense Fine-Grained Event Recognition, Localisation and Description

Faulkner, Hayden; Dick, Anthony

doi:10.1109/dicta.2017.8227494

Cited by 8 publications

(5 citation statements)

References 36 publications

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“…TenniSet [195] comprises five tennis videos of 2012 London Olympic matches from YouTube and six categories of events are considered, such as set, hit and serve. The time boundary of each event is labeled, therefore, it can be used for both recognition and localization.…”

Section: E Tennismentioning

confidence: 99%

“…One recent work -Temporal Query Networks (TQN) [283] combines 3D CNNs and transformers. Specifically, 3D CNNs are used as the backbone to extract video features and transformers are adopted as decoders, i.e., given a query, the transformers [190] Lightweight 3D [280] 2022 90.9 TenniSet [195] Two-stream [195] 2017 81.0 output a response, where the queries are texts like the number of flips for diving and the responses are the corresponding attributes, such as a number or a label. The transformer-based decoder models the relevance among visual features, queries and responses.…”

Section: B Deep Modelsmentioning

confidence: 99%

“…Further we showcase two representative datasets, which are table tennis videos (P As we mentioned in Applications section, the action recognition techniques are widely used in web or TV streaming for the purpose of Video Highlights. While the videos are normally recorded via multiple cameras and are in different views [2], [101], [145], [183], [191], [195], [213], this requires the robustness and adaptability of the corresponding action recognition algorithms. Via a thorough investigation in this paper, most of the benchmarks [13], [107], [191], [234]- [239] of action recognition on video datasets focus on single-camera or single-view actions, where it does not conform with the format of sports videos.…”

Section: Challengesmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

Wu¹,

Wang²,

Bian³

et al. 2022

Preprint

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To understand human behaviors, action recognition based on videos is a common approach. Compared with imagebased action recognition, videos provide much more information. Reducing the ambiguity of actions and in the last decade, many works focused on datasets, novel models and learning approaches have improved video action recognition to a higher level. However, there are challenges and unsolved problems, in particular in sports analytics where data collection and labeling are more sophisticated, requiring sport professionals to annotate data. In addition, the actions could be extremely fast and it becomes difficult to recognize them. Moreover, in team sports like football and basketball, one action could involve multiple players, and to correctly recognize them, we need to analyse all players, which is relatively complicated. In this paper, we present a survey on video action recognition for sports analytics. We introduce more than ten types of sports, including team sports, such as football, basketball, volleyball, hockey and individual sports, such as figure skating, gymnastics, table tennis, tennis, diving and badminton. Then we compare numerous existing frameworks for sports analysis to present status quo of video action recognition in both team sports and individual sports. Finally, we discuss the challenges and unsolved problems in this area and to facilitate sports analytics, we develop a toolbox using PaddlePaddle 1 , which supports football, basketball, table tennis and figure skating action recognition.

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Section: E Tennismentioning

confidence: 99%

Section: B Deep Modelsmentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

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A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

Wu¹,

Wang²,

Bian³

et al. 2022

Preprint

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“…• Comparing to the table tennis datasets [14,51], P 2 A has an adequate sample size (10 times ∼ 200 times) and more subtle set of categories. • Comparing to the large datasets in other sports domain [6,38,42,45], P 2 A focuses on the dense (5 times ∼ 50 times) and fastmoving (around 0.1 times) actions.…”

Section: Dataset Analysismentioning

confidence: 99%

$\textbf{P$^2$A}$: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos

Bian¹,

Wang²,

Xiong³

et al. 2022

Preprint

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While deep learning has been widely used for video analytics, such as video classification and action detection, dense action detection with fast-moving subjects from sports videos is still challenging. In this work, we release yet another sports video dataset P 2 A for Ping Pong-Action detection, which consists of 2,721 video clips collected from the broadcasting videos of professional table tennis matches in World Table Tennis Championships and Olympiads. We work with a crew of table tennis professionals and referees to obtain fine-grained action labels (in 14 classes) for every ping-pong action appeared in the dataset, and formulate two sets of action detection problems-action localization and action recognition. We evaluate a number of commonly-seen action recognition (e.g., TSM, TSN, Video SwinTransformer, and Slowfast) and action localization models (e.g., BSN, BSN++, BMN, TCANet), using P 2 A for both problems, under various settings. These models can only achieve 48% area under the AR-AN curve for localization and 82% top-one accuracy for recognition, since the ping-pong actions are dense with fast-moving subjects but broadcasting videos are with only 25 FPS. The results confirm that P 2 A is still a challenging task and can be used as a benchmark for action detection from videos. CCS CONCEPTS• Computing methodologies → Activity recognition and understanding; Video segmentation.

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“…THETIS [256] includes 1,980 self-recorded videos of 12 tennis actions with RGB, depth, 2D skeleton, and 3D skeleton videos, which can be used for multiple types of action recognition models. TenniSet [257] contains five Olympic tennis match videos with six labeled event categories and textural descriptions, making it suitable for both recognition, localization, and action retrieval tasks.…”

Section: E Tennismentioning

confidence: 99%

Poster Abstract: API QoS Prediction for Apps in Cellular Networks

Zhao¹,

Wang²,

Wang³

2016

2016 IEEE/ACM Symposium on Edge Computing (SEC)

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Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports.Our survey provides valuable reference material for researchers interested in deep learning in sports applications.

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TenniSet: A Dataset for Dense Fine-Grained Event Recognition, Localisation and Description

Cited by 8 publications

References 36 publications

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

$\textbf{P$^2$A}$: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos

Poster Abstract: API QoS Prediction for Apps in Cellular Networks

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