Athlete Pose Estimation by a Global-Local Network

Hwang, Jee Youn; Park, Sungheon; Kwak, Nojun

doi:10.1109/cvprw.2017.20

Cited by 19 publications

(11 citation statements)

References 13 publications

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“…Several approaches are trying to solve specific estimation problems in different environments, such as the ones for basketball [ 14 ], diving [ 24 ], hockey [ 35 ], etc, while others try to create a general sports use system, such as [ 39 , 41 ]. Taking into account the limited amount of work in specific sports, we can say that interesting research and development can be found regarding HPE and hockey .…”

Section: Discussionmentioning

confidence: 99%

“…The segments of the body are annotated manually . Athlete pose estimation by a global-local network [ 39 ] HPE of athletes using a global-local approach. RGB images Output: 2D Publicly available datasets: LSP for quantitative and qualitative HPE evaluation and UCF for qualitative evaluation, as this last dataset is used for sports action recognition, so, it does not include any joint position annotation.…”

Section: Table A1mentioning

confidence: 99%

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A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise

Badiola-Bengoa

Zorrilla

2021

Sensors

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Human Pose Estimation (HPE) has received considerable attention during the past years, improving its performance thanks to the use of Deep Learning, and introducing new interesting uses, such as its application in Sport and Physical Exercise (SPE). The aim of this systematic review is to analyze the literature related to the application of HPE in SPE, the available data, methods, performance, opportunities, and challenges. One reviewer applied different inclusion and exclusion criteria, as well as quality metrics, to perform the paper filtering through the paper databases. The Association for Computing Machinery Digital Library, Web of Science, and dblp included more than 500 related papers after the initial filtering, finally resulting in 20. In addition, research was carried out regarding the publicly available data related to this topic. It can be concluded that even if related public data can be found, much more data is needed to be able to obtain good performance in different contexts. In relation with the methods of the authors, the use of general purpose systems as base, such as Openpose, combined with other methods and adaptations to the specific use case can be found. Finally, the limitations, opportunities, and challenges are presented.

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Section: Discussionmentioning

confidence: 99%

Section: Table A1mentioning

confidence: 99%

A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise

Badiola-Bengoa

Zorrilla

2021

Sensors

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“…Secondly, visual analysis was used to conduct the pose estimation of the athletes. Hwang et al [28] proposed an athlete pose estimation method by combining global and local information using the 2D image. Chen et al [27] utilized Kinect video analysis for rectifying yoga postures in a self-training system.…”

Section: Related Work a Visual Analysis In Sportsmentioning

confidence: 99%

Object-Level Trajectories Based Fine-Grained Action Recognition in Visual IoT Applications

Xiong

Wang

et al. 2019

IEEE Access

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The emerging computer vision and deep learning technologies are being applied to the intelligent analysis of sports training videos. In this paper, a deep learning based fine-grained action recognition (FGAR) method is proposed to analyze soccer training videos. The proposed method was applied to indoor training equipment for evaluating whether a player has stopped a soccer ball successfully or not. First, the problem of FGAR is modeled as human-object (player-ball) interactions. The object-level trajectories are proposed as a new descriptor to identify fine-grained sports videos. The proposed descriptor can take advantage of high-level semantic and human-object interaction motions. Second, a cascaded scheme of deep networks based on the object-level trajectories is proposed to realize FGAR. The cascaded network is constructed by concatenating a detector network with a classifier network (a long-short-termmemory (LSTM)-based network). The cascaded scheme takes the advantage of the high efficiency of the detector on object detection and the outstanding performance of the LSTM-based network on processing time series. The experimental results show that the proposed method can achieve an accuracy of 93.24%.

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“…Computer vision has been adopted for various applications in the sports domain. Prominent tasks include sports type [8] and activity recognition [17,27], tracking athletes and other objects of interest in videos [24,31] and human pose estimation [6,11]. [15] offer an overview of a wide range of application.…”

Section: Related Workmentioning

confidence: 99%

“…[26] describe an architecture that directly regresses the image coordinates of body joints. Subsequent publications regress confidence maps that indicate the likelihood of all possible joint locations in an image [11,16,18,20]. This spatial encoding of the learning objective seems to be more natural to CNNs compared to the direct regression of image coordinates.…”

Section: Related Workmentioning

confidence: 99%

Activity-Conditioned Continuous Human Pose Estimation for Performance Analysis of Athletes Using the Example of Swimming

Einfalt¹,

Zecha²,

Lienhart³

2018

2018 IEEE Winter Conference on Applications of Computer Vision (WACV)

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In this paper we consider the problem of human pose estimation in real-world videos of swimmers. Swimming channels allow filming swimmers simultaneously above and below the water surface with a single stationary camera. These recordings can be used to quantitatively assess the athletes' performance. The quantitative evaluation, so far, requires manual annotations of body parts in each video frame. We therefore apply the concept of CNNs in order to automatically infer the required pose information. Starting with an off-the-shelf architecture, we develop extensions to leverage activity information -in our case the swimming style of an athlete -and the continuous nature of the video recordings. Our main contributions are threefold: (a) We apply and evaluate a fine-tuned Convolutional Pose Machine architecture as a baseline in our very challenging aquatic environment and discuss its error modes, (b) we propose an extension to input swimming style information into the fully convolutional architecture and (c) modify the architecture for continuous pose estimation in videos. With these additions we achieve reliable pose estimates with up to +16% more correct body joint detections compared to the baseline architecture.

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Athlete Pose Estimation by a Global-Local Network

Cited by 19 publications

References 13 publications

A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise

A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise

Object-Level Trajectories Based Fine-Grained Action Recognition in Visual IoT Applications

Activity-Conditioned Continuous Human Pose Estimation for Performance Analysis of Athletes Using the Example of Swimming

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