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arXiv · 2106.15304

Towards Fast and Accurate Multi-Person Pose Estimation on Mobile Devices

Xuan Shen·Geng Yuan·Wei Niu·Xiaolong Ma·Jiexiong Guan·Zhengang Li·Bin Ren·Yanzhi Wang
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Abstract

The rapid development of autonomous driving, abnormal behavior detection, and behavior recognition makes an increasing demand for multi-person pose estimation-based applications, especially on mobile platforms. However, to achieve high accuracy, state-of-the-art methods tend to have a large model size and complex post-processing algorithm, which costs intense computation and long end-to-end latency. To solve this problem, we propose an architecture optimization and weight pruning framework to accelerate inference of multi-person pose estimation on mobile devices. With our optimization framework, we achieve up to 2.51x faster model inference speed with higher accuracy compared to representative lightweight multi-person pose estimator.

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Xuan Shen, Geng Yuan, Wei Niu, Xiaolong Ma, Jiexiong Guan, Zhengang Li, Bin Ren, Yanzhi Wang. 2021-06-06. Towards Fast and Accurate Multi-Person Pose Estimation on Mobile Devices. https://arxiv.org/abs/2106.15304

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