AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time

Fang, Hao-Shu; Li, Jiefeng; Tang, Huiying; Xu, Chao; Zhu, Haoyi; Xiu, Yuliang; Li, Yong-Lu; Lu, Cewu

doi:10.1109/tpami.2022.3222784

Cited by 229 publications

(92 citation statements)

References 81 publications

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“…After image processing, the algorithm integrating YOLOv3 and AlphaPose [ 75 ] was used to detect human poses. The algorithm includes the three main components [ 76 ].…”

Section: Resultsmentioning

confidence: 99%

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

Paulauskaitė-Tarasevičienė

Siaulys

Šutienė

et al. 2023

Healthcare

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The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients’ data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient’s position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.

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“…After image processing, the algorithm integrating YOLOv3 and AlphaPose [ 75 ] was used to detect human poses. The algorithm includes the three main components [ 76 ].…”

Section: Resultsmentioning

confidence: 99%

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

Paulauskaitė-Tarasevičienė

Siaulys

Šutienė

et al. 2023

Healthcare

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“…One of the first approaches to do so, was a declination of Mask-RCNN, which added a joint regression head to its instance segmentation backbone [ 44 ]. Noteworthy methods from the state-of-the-art such as UniPose [ 20 ], OpenPose [ 45 ] or Alpha-Pose [ 46 ]. In this work we adopt UniPose [ 20 ] as a source for extracting body joints and train our model to estimate action progress.…”

Section: Related Workmentioning

confidence: 99%

Joint-Based Action Progress Prediction

Pucci

Becattini

Bimbo

2023

Sensors

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Action understanding is a fundamental computer vision branch for several applications, ranging from surveillance to robotics. Most works deal with localizing and recognizing the action in both time and space, without providing a characterization of its evolution. Recent works have addressed the prediction of action progress, which is an estimate of how far the action has advanced as it is performed. In this paper, we propose to predict action progress using a different modality compared to previous methods: body joints. Human body joints carry very precise information about human poses, which we believe are a much more lightweight and effective way of characterizing actions and therefore their execution. Estimating action progress can in fact be determined based on the understanding of how key poses follow each other during the development of an activity. We show how an action progress prediction model can exploit body joints and integrate it with modules providing keypoint and action information in order to be run directly from raw pixels. The proposed method is experimentally validated on the Penn Action Dataset.

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“…For MMPose, each image is cropped at the bounding box and this region is passed to a 2D keypoint detector. In this work, the specific architecture we use is an HRNet [21] with a channel width of 48 that is pretrained on the Halpe dataset [22]. We selected this because, in contrast to the commonly used COCO dataset Fig.…”

Section: Data Acquisitionmentioning

confidence: 99%

Improved Trajectory Reconstruction for Markerless Pose Estimation

Cotton¹,

Anthony²,

Shah³

et al. 2023

Preprint

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Markerless pose estimation allows reconstructing human movement from multiple synchronized and calibrated views, and has the potential to make movement analysis easy and quick, including gait analysis. This could enable much more frequent and quantitative characterization of gait impairments, allowing better monitoring of outcomes and responses to interventions. However, the impact of different keypoint detectors and reconstruction algorithms on markerless pose estimation accuracy has not been thoroughly evaluated. We tested these algorithmic choices on data acquired from a multicamera system from a heterogeneous sample of 25 individuals seen in a rehabilitation hospital. We found that using a topdown keypoint detector and reconstructing trajectories with an implicit function enabled accurate, smooth and anatomically plausible trajectories, with a noise in the step width estimates compared to a GaitRite walkway of only 8mm.

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AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time

Cited by 229 publications

References 81 publications

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

Joint-Based Action Progress Prediction

Improved Trajectory Reconstruction for Markerless Pose Estimation

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