DUA: A Domain-Unified Approach for Cross-Dataset 3D Human Pose Estimation

Manesco, João Renato Ribeiro; Berretti, Stefano; Marana, Aparecido Nilceu

doi:10.3390/s23177312

Cited by 2 publications

(1 citation statement)

References 44 publications

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“…This method is unaffected by camera direction and has shown improvements in single-view human pose estimators trained using limited labeled 3D pose data. Manesco et al [11] (2023) proposed a novel approach called the domain unified method, aiming to address pose misalignment in cross-dataset scenarios through a combination of three modules on top of a pose estimator, including a pose transformer, uncertainty estimator, and domain classifier. Li et al [12] (2023) presented a hybrid model that combines convolution and transformation models to address the inconsistency between the performances of key point localization with higher accuracy and overall performance.…”

Section: Introduction 21 Research Work By Relevant Scholarsmentioning

confidence: 99%

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

Ding,

Niu,

Nie

et al. 2024

Sensors

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In response to the numerous challenges faced by traditional human pose recognition methods in practical applications, such as dense targets, severe edge occlusion, limited application scenarios, complex backgrounds, and poor recognition accuracy when targets are occluded, this paper proposes a YOLO-Pose algorithm for human pose estimation. The specific improvements are divided into four parts. Firstly, in the Backbone section of the YOLO-Pose model, lightweight GhostNet modules are introduced to reduce the model’s parameter count and computational requirements, making it suitable for deployment on unmanned aerial vehicles (UAVs). Secondly, the ACmix attention mechanism is integrated into the Neck section to improve detection speed during object judgment and localization. Furthermore, in the Head section, key points are optimized using coordinate attention mechanisms, significantly enhancing key point localization accuracy. Lastly, the paper improves the loss function and confidence function to enhance the model’s robustness. Experimental results demonstrate that the improved model achieves a 95.58% improvement in mAP50 and a 69.54% improvement in mAP50-95 compared to the original model, with a reduction of 14.6 M parameters. The model achieves a detection speed of 19.9 ms per image, optimized by 30% and 39.5% compared to the original model. Comparisons with other algorithms such as Faster R-CNN, SSD, YOLOv4, and YOLOv7 demonstrate varying degrees of performance improvement.

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Section: Introduction 21 Research Work By Relevant Scholarsmentioning

confidence: 99%

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

Ding,

Niu,

Nie

et al. 2024

Sensors

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3D Human Pose Estimation Based on Monocular RGB Images and Domain Adaptation

Manesco,

Berretti,

Marana

2024

Anais Estendidos Da XXXVII Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2024)

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Human pose estimation in monocular images is a challenging problem in Computer Vision. Currently, while 2D poses find extensive applications, the use of 3D poses suffers from data scarcity due to the difficulty of acquisition. Therefore, fully convolutional approaches struggle due to limited 3D pose labels, prompting a two-step strategy leveraging 2D pose estimators, which does not generalize well to unseen poses, requiring the use of domain adaptation techniques. In this work, we introduce a novel Domain Unified Approach called DUA, which, through a unique combination of three modules on top of the pose estimator (pose converter, uncertainty estimator, and domain classifier), can improve the accuracy of 3D poses estimated from 2D poses. In the experiments carried out with SURREAL and Human3.6M datasets, our method reduced the mean per-joint position error (MPJPE) by 44.1 mm in the synthetic-to-real scenario, a quite significant result. Furthermore, our method outperformed all state-of-the-art methods in the real-to-synthetic scenario.

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DUA: A Domain-Unified Approach for Cross-Dataset 3D Human Pose Estimation

Cited by 2 publications

References 44 publications

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

Research on Human Posture Estimation Algorithm Based on YOLO-Pose

3D Human Pose Estimation Based on Monocular RGB Images and Domain Adaptation

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