Application of Computer Vision and Vector Space Model for Tactical Movement Classification in Badminton

Weeratunga, Kokum; Dharmaratne, Anuja; How, Khoo Boon

doi:10.1109/cvprw.2017.22

Cited by 15 publications

(8 citation statements)

References 20 publications

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“…Similar key issues, including the motion detection and segmentation, feature extraction, and feature similarity measurement, have to be tackled for action evaluation. In early studies in this field, most research works [1,2,3,4,5,6,7] directly introduced and fine-tuned state-of-the-art action recognition approaches to deal with action evaluation problems. They either regarded action evaluation as a classification problem or simply replaced the optimization function of classification with regressions in the final stage without analyzing the essential differences between two tasks.…”

Section: Overview Of Human Action Evaluation Methodsmentioning

confidence: 99%

“…Human action evaluation (HAE) [1,2,3,4,5,6,7,8,9,10,48,49,50] aims to design computation models for representing the dynamic changing process of human movement and to develop evaluation techniques to measure the completion quality of human action. The target of action evaluation not only requires the recognition of the action performed but also more significantly, needs to provide a quality assessment of how the action was performed.…”

Section: Problem Definitionmentioning

confidence: 99%

“…In recent years, human action evaluation has emerged as an important problem in a variety of computer vision applications, which range from sports training [1,2,3,4,5] to healthcare and physical rehabilitation [6,7,8,9], interactive entertainment [10,11,12], and video understanding [13,14,15]. In contrast to the aims of traditional human action recognition to infer the class label from predefined action categories (action classification [16,17]), to locate the starting and end positions of actions (action detection [18,19]), and to predict the future state of actions on the basis of incomplete action observations (action prediction [20,21]), the target of human action evaluation is to make computers automatically quantify how well people perform actions and further provide interpretable feedback for improving the motion of the human body.…”

Section: Introductionmentioning

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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Section: Overview Of Human Action Evaluation Methodsmentioning

confidence: 99%

Section: Problem Definitionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Analyzing characteristics of players: Weeratunga et al [21] proposed a method to analyze badminton tactics from video sequences. It is based on players' trajectory during the play.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos

Chaudhury¹,

Kimura²,

Vinayavekhin³

et al. 2019

2019 IEEE International Symposium on Multimedia (ISM)

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Image-based sports analytics enable automatic retrieval of key events in a game to speed up the analytics process for human experts. However, most existing methods focus on structured television broadcast video datasets with a straight and fixed camera having minimum variability in the capturing pose. In this paper, we study the case of event detection in sports videos for unstructured environments with arbitrary camera angles. The transition from structured to unstructured video analysis produces multiple challenges that we address in our paper. Specifically, we identify and solve two major problems: unsupervised identification of players in an unstructured setting and generalization of the trained models to pose variations due to arbitrary shooting angles. For the first problem, we propose a temporal feature aggregation algorithm using person re-identification features to obtain high player retrieval precision by boosting a weak heuristic scoring method. Additionally, we propose a data augmentation technique, based on multi-modal image translation model, to reduce bias in the appearance of training samples. Experimental evaluations show that our proposed method improves precision for player retrieval from 0.78 to 0.86 for obliquely angled videos. Additionally, we obtain an improvement in F1 score for rally detection in table tennis videos from 0.79 in case of global frame-level features to 0.89 using our proposed player-level features. Please see the supplementary video submission at https://ibm.biz/BdzeZA.

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“…The aim of this category of problems is to design a computational model that captures the dynamic changes in human movement and measures the quality of human actions based on a predefined metric. HAE has been studied in a variety of computer vision applications such as sports activity scoring, athletes training [35,48,30], rehabilitation and healthcare [29,2], interactive games [53,25], skill assessment [20,8], and workers activity assessment in industrial settings [33,32]. Some of the earlier works on * To appear at the 2021 Winter Conference on Applications of Computer Vision (WACV '21).…”

Section: Introductionmentioning

confidence: 99%

A Multi-Task Learning Approach for Human Activity Segmentation and Ergonomics Risk Assessment

Parsa¹,

Banerjee²

2020

Preprint

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We propose a new approach to Human Action Evaluation (HAE) in long videos using graph-based multi-task modeling. Previous works in activity assessment either directly compute a metric using a detected skeleton or use the scene information to regress the activity score. These approaches are insufficient for accurate activity assessment since they only compute an average score over a clip, and do not consider the correlation between the joints and body dynamics. Moreover, they are highly scene-dependent which makes the generalizability of these methods questionable. We propose a novel multi-task framework for HAE that utilizes a Graph Convolutional Network backbone to embed the interconnection between human joints in the features. In this framework, we solve the Human Action Detection (HAD) problem as an auxiliary task to improve activity assessment. The HAD head is powered by an Encoder-Decoder Temporal Convolutional Network to detect activities in long videos and HAE uses a Long-Short-Term-Memory-based architecture. We evaluate our method on the UW-IOM and TUM Kitchen datasets and discuss the success and failure cases on these two datasets.

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Application of Computer Vision and Vector Space Model for Tactical Movement Classification in Badminton

Cited by 15 publications

References 20 publications

A Survey of Vision-Based Human Action Evaluation Methods

A Survey of Vision-Based Human Action Evaluation Methods

Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos

A Multi-Task Learning Approach for Human Activity Segmentation and Ergonomics Risk Assessment

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