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EfficientPose: Scalable single-person pose estimation
Applied Intelligence ( IF 5.3 ) Pub Date : 2020-11-06 , DOI: 10.1007/s10489-020-01918-7
Daniel Groos , Heri Ramampiaro , Espen AF Ihlen

Single-person human pose estimation facilitates markerless movement analysis in sports, as well as in clinical applications. Still, state-of-the-art models for human pose estimation generally do not meet the requirements of real-life applications. The proliferation of deep learning techniques has resulted in the development of many advanced approaches. However, with the progresses in the field, more complex and inefficient models have also been introduced, which have caused tremendous increases in computational demands. To cope with these complexity and inefficiency challenges, we propose a novel convolutional neural network architecture, called EfficientPose, which exploits recently proposed EfficientNets in order to deliver efficient and scalable single-person pose estimation. EfficientPose is a family of models harnessing an effective multi-scale feature extractor and computationally efficient detection blocks using mobile inverted bottleneck convolutions, while at the same time ensuring that the precision of the pose configurations is still improved. Due to its low complexity and efficiency, EfficientPose enables real-world applications on edge devices by limiting the memory footprint and computational cost. The results from our experiments, using the challenging MPII single-person benchmark, show that the proposed EfficientPose models substantially outperform the widely-used OpenPose model both in terms of accuracy and computational efficiency. In particular, our top-performing model achieves state-of-the-art accuracy on single-person MPII, with low-complexity ConvNets.



中文翻译:

EfficientPose:可扩展的单人姿势估计

单人人体姿势估计有助于在运动以及临床应用中进行无标记运动分析。尽管如此,用于人体姿势估计的最新模型通常仍不能满足实际应用的要求。深度学习技术的激增导致许多高级方法的发展。但是,随着该领域的进展,还引入了更复杂和效率较低的模型,这导致计算需求的巨大增加。为了应对这些复杂性和效率低下的挑战,我们提出了一种新颖的卷积神经网络体系结构,称为EfficientPose,该体系结构利用了最近提出的EfficientNets,以提供高效且可扩展的单人姿势估计。EfficientPose是一系列模型,这些模型利用有效的多尺度特征提取器和使用移动倒置瓶颈卷积的计算效率高的检测块,同时确保仍然提高姿势配置的精度。由于其低复杂度和效率,EfficientPose通过限制内存占用量和计算成本来支持边缘设备上的实际应用。我们的实验结果使用具有挑战性的MPII单人基准测试,结果表明,在准确性和计算效率方面,所提出的EfficientPose模型在本质上优于广泛使用的OpenPose模型。尤其是,我们的性能最高的模型具有低复杂度的ConvNets,可在单人MPII上实现最先进的准确性。

更新日期:2020-11-06
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