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Lightweight Architecture for Real-Time Hand Pose Estimation with Deep Supervision

1
School of Software, Shanghai Jiao Tong University, Shanghai 200240, China
2
Internet of Things Group, Intel Corporation, Shanghai 200131, China
3
School of Software, Tongji University, Shanghai 200072, China
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(4), 585; https://doi.org/10.3390/sym11040585
Received: 7 March 2019 / Revised: 17 April 2019 / Accepted: 18 April 2019 / Published: 23 April 2019
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Abstract

The high demand for computational resources severely hinders the deployment of deep learning applications in resource-limited devices. In this work, we investigate the under-studied but practically important network efficiency problem and present a new, lightweight architecture for hand pose estimation. Our architecture is essentially a deeply-supervised pruned network in which less important layers and branches are removed to achieve a higher real-time inference target on resource-constrained devices without much accuracy compromise. We further make deployment optimization to facilitate the parallel execution capability of central processing units (CPUs). We conduct experiments on NYU and ICVL datasets and develop a demo1 using the RealSense camera. Experimental results show our lightweight network achieves an average running time of 32 ms (31.3 FPS, the original is 22.7 FPS) before deployment optimization. Meanwhile, the model is only about half parameters size of the original one with 11.9 mm mean joint error. After the further optimization with OpenVINO, the optimized model can run at 56 FPS on CPUs in contrast to 44 FPS running on a graphics processing unit (GPU) (Tensorflow) and it can achieve the real-time goal. View Full-Text
Keywords: hourglass; 3D regression; deep supervision; prune; deployment optimization; convolutional neural network hourglass; 3D regression; deep supervision; prune; deployment optimization; convolutional neural network
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Wu, Y.; Ruan, X.; Zhang, Y.; Zhou, H.; Du, S.; Wu, G. Lightweight Architecture for Real-Time Hand Pose Estimation with Deep Supervision. Symmetry 2019, 11, 585.

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