Post-Data Augmentation to Improve Deep Pose Estimation of Extreme and Wild Motions

Toyoda, Kohei; Kono, Michinari; Rekimoto, Jun

doi:10.1109/vr.2019.8798221

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…Another recurrent problem when applying HPE to different sports is the huge error when rare poses are present, such as in gymnastics, pole vault, swimming, dance, etc. There are some papers, such as [ 63 ], that try to lower the problem using data augmentation methods, but there is still a lot of work to do on this topic.…”

Section: Discussionmentioning

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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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%

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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“…To address this, Peng et al [194] designed an adversarial data augmentation network based on Generative Adversarial Networks (GANs) [47], and reinforcement learning [111] and achieved improved performance by optimising them together. Toyoda [181] achieved improved HPE performance on images involving extreme and wild motions by implementing rotation augmentation. In addition to overfitting, person detectors often return redundant detections which may raise IoU scores in person detectors.…”

Section: The 2d Mppe: Two-stage Top-down Approachmentioning

confidence: 99%

Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models

et al. 2022

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Person identification is a problem that has received substantial attention, particularly in security domains. Gait recognition is one of the most convenient approaches enabling person identification at a distance without the need of high-quality images. There are several review studies addressing person identification such as the utilization of facial images, silhouette images, and wearable sensor. Despite skeleton-based person identification gaining popularity while overcoming the challenges of traditional approaches, existing survey studies lack the comprehensive review of skeleton-based approaches to gait identification. We present a detailed review of the human pose estimation and gait analysis that make the skeleton-based approaches possible. The study covers various types of related datasets, tools, methodologies, and evaluation metrics with associated challenges, limitations, and application domains. Detailed comparisons are presented for each of these aspects with recommendations for potential research and alternatives. A common trend throughout this paper is the positive impact that deep learning techniques are beginning to have on topics such as human pose estimation and gait identification. The survey outcomes might be useful for the related research community and other stakeholders in terms of performance analysis of existing methodologies, potential research gaps, application domains, and possible contributions in the future.

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“…They predicted three two-dimensional marginal heat maps per joint under an augmented soft-argmax scheme. Using post-data augmentation techniques to improve the quality of extreme/wild motions' pose estimation, Toyoda et al [31] proposed a method that augmented the input data with rotation augmentation, then applied the pose estimation technique multiple times for every frame. The most consistent pose was then selected followed by a motion reconstruction for smoothing.…”

Section: Deep Hpe Approachesmentioning

confidence: 99%

Deep Full-Body HPE for Activity Recognition from RGB Frames Only

Boualia

Amara

2021

Informatics

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Human Pose Estimation (HPE) is defined as the problem of human joints’ localization (also known as keypoints: elbows, wrists, etc.) in images or videos. It is also defined as the search for a specific pose in space of all articulated joints. HPE has recently received significant attention from the scientific community. The main reason behind this trend is that pose estimation is considered as a key step for many computer vision tasks. Although many approaches have reported promising results, this domain remains largely unsolved due to several challenges such as occlusions, small and barely visible joints, and variations in clothing and lighting. In the last few years, the power of deep neural networks has been demonstrated in a wide variety of computer vision problems and especially the HPE task. In this context, we present in this paper a Deep Full-Body-HPE (DFB-HPE) approach from RGB images only. Based on ConvNets, fifteen human joint positions are predicted and can be further exploited for a large range of applications such as gesture recognition, sports performance analysis, or human-robot interaction. To evaluate the proposed deep pose estimation model, we apply it to recognize the daily activities of a person in an unconstrained environment. Therefore, the extracted features, represented by deep estimated poses, are fed to an SVM classifier. To validate the proposed architecture, our approach is tested on two publicly available benchmarks for pose estimation and activity recognition, namely the J-HMDBand CAD-60datasets. The obtained results demonstrate the efficiency of the proposed method based on ConvNets and SVM and prove how deep pose estimation can improve the recognition accuracy. By means of comparison with state-of-the-art methods, we achieve the best HPE performance, as well as the best activity recognition precision on the CAD-60 dataset.

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Post-Data Augmentation to Improve Deep Pose Estimation of Extreme and Wild Motions

Cited by 9 publications

References 14 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

Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models

Deep Full-Body HPE for Activity Recognition from RGB Frames Only

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