---
title: "Towards Fast and Accurate Multi-Person Pose Estimation on Mobile Devices"
authors:
  - "Xuan Shen"
  - "Geng Yuan"
  - "Wei Niu"
  - "Xiaolong Ma"
  - "Jiexiong Guan"
  - "Zhengang Li"
  - "Bin Ren"
  - "Yanzhi Wang"
doi: "10.24963/ijcai.2021/715"
url: "https://ijcai.org/proceedings/2021/715"
pdf: "https://ijcai.org/proceedings/2021/0715.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "5012-5015"
track: "Demo Track"
keywords:
  - "Human-Computer Interaction: General"
  - "Computer Vision: General"
  - "Machine Learning: General"
---

# 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

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 5012-5015. DOI: [10.24963/ijcai.2021/715](https://doi.org/10.24963/ijcai.2021/715)

## 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.