Analyzing Diving: A Dataset for Judging Action Quality

Wnuk, Kamil; Soatto, Stefano

doi:10.1007/978-3-642-22822-3_27

Cited by 12 publications

(9 citation statements)

References 13 publications

(12 reference statements)

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“…Given these biases that seem inherent in judgements of sequences, one method of tackling the issue might be to remove the human element altogether by automating the process. Automatic diving analysis using computational techniques, once sufficiently advanced/reliable, could make the whole process of judging these types of events one of objective measurements [ 35 , 36 ]. This would bring competitions involving judgements of abilities into line with events where performances are already judged automatically (e.g.…”

Section: Discussionmentioning

confidence: 99%

Sequential effects in Olympic synchronized diving scores

Kramer

2017

R. Soc. open sci.

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When judging performances in a sequence, the current score is often influenced by the preceding score. Where athletes are perceived to be similar, a judgement is assimilated towards the previous one. However, if judges focus on the differences between the two athletes, this will result in a contrasting influence on their scores. Here, I investigate sequential effects during synchronized diving events at the 2012 and 2016 Olympic Games. Although previous research found assimilation in scores of gymnasts, the current data showed contrast effects—current scores benefited from following a poor performance but were at a disadvantage if they followed a high-scoring performance. One explanation may be that the processes involved in judging synchronized pairs results in a focus on the differences between athletes, producing a contrast effect across dives. That the specific direction of this sequential bias may depend on the particular sport has implications for how judges might approach their roles in a context-dependent manner, as well as how such biases should be addressed.

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Section: Discussionmentioning

confidence: 99%

Sequential effects in Olympic synchronized diving scores

Kramer

2017

R. Soc. open sci.

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“…Wnuk and Soatto [79] created the first diving dataset, FINA09, which was gathered from the finals of the diving event at the 13th FINA World Championships. The dataset consists of 68 diving videos performed by 12 different divers with 10 dive types, including 107B, 205B, and 307C.…”

Section: Overview Of Human Action Evaluation Methodsmentioning

confidence: 99%

“…In the research on automatic sports activity scoring, several sports activity datasets have been collected from international competition events on diving, figure skating, vaulting, and others. Several publicly available benchmark datasets are introduced, including the FINA09 diving dataset [79], MIT Olympic scoring dataset [1], UNLV AQA-7 dataset [95], MTL-AQA dataset [96], and Fis-V dataset [97], etc. The details of these benchmarks are presented in Table 3 from the perspective of action categories, the number of samples, data source, viewpoint, and variation in the background.…”

Section: Benchmark Datasets and Evaluation Criteriamentioning

confidence: 99%

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A Survey of Vision-Based Human Action Evaluation Methods

Lei

Zhang

et al. 2019

Sensors

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The fields of human activity analysis have recently begun to diversify. Many researchers have taken much interest in developing action recognition or action prediction methods. The research on human action evaluation differs by aiming to design computation models and evaluation approaches for automatically assessing the quality of human actions. This line of study has become popular because of its explosively emerging real-world applications, such as physical rehabilitation, assistive living for elderly people, skill training on self-learning platforms, and sports activity scoring. This paper presents a comprehensive survey of approaches and techniques in action evaluation research, including motion detection and preprocessing using skeleton data, handcrafted feature representation methods, and deep learning-based feature representation methods. The benchmark datasets from this research field and some evaluation criteria employed to validate the algorithms’ performance are introduced. Finally, the authors present several promising future directions for further studies.

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“…Only a handful of works directly address the problem of action quality assessment [7,6,11,17,14]. Wnuk and Soatto introduced the FINA09 diving dataset, perform a pilot study, and conclude that, temporal information, which is implicitly present in videos, is vital, and estimation of human pose using HoG features is reliable [14]. Human pose features have also been used in [7,11] to assess action quality of Olympic sporting events.…”

Section: Related Workmentioning

confidence: 99%

Learning to Score Olympic Events

Parmar

Morris

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

161

175

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Estimating action quality, the process of assigning a "score" to the execution of an action, is crucial in areas such as sports and health care.Unlike action recognition, which has millions of examples to learn from, the action quality datasets that are currently available are small -typically comprised of only a few hundred samples. This work presents three frameworks for evaluating Olympic sports which utilize spatiotemporal features learned using 3D convolutional neural networks (C3D) and perform score regression with i) SVR, ii) LSTM, and iii) LSTM followed by SVR. An efficient training mechanism for the limited data scenarios is presented for clip-based training with LSTM. The proposed systems show significant improvement over existing quality assessment approaches on the task of predicting scores of Olympic events {diving, vault, figure skating}.While the SVR-based frameworks yield better results, LSTM-based frameworks are more natural for describing an action and can be used for improvement feedback.

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Analyzing Diving: A Dataset for Judging Action Quality

Cited by 12 publications

References 13 publications

Sequential effects in Olympic synchronized diving scores

Sequential effects in Olympic synchronized diving scores

A Survey of Vision-Based Human Action Evaluation Methods

Learning to Score Olympic Events

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