DIR-BHRNet: A Lightweight Network for Real-time Vision-based Multi-person Pose Estimation on Smartphones
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Lan, Gongjin Wu, Yu Hao, Qi |
| author_facet | Lan, Gongjin Wu, Yu Hao, Qi |
| contents | Human pose estimation (HPE), particularly multi-person pose estimation (MPPE), has been applied in many domains such as human-machine systems. However, the current MPPE methods generally run on powerful GPU systems and take a lot of computational costs. Real-time MPPE on mobile devices with low-performance computing is a challenging task. In this paper, we propose a lightweight neural network, DIR-BHRNet, for real-time MPPE on smartphones. In DIR-BHRNet, we design a novel lightweight convolutional module, Dense Inverted Residual (DIR), to improve accuracy by adding a depthwise convolution and a shortcut connection into the well-known Inverted Residual, and a novel efficient neural network structure, Balanced HRNet (BHRNet), to reduce computational costs by reconfiguring the proper number of convolutional blocks on each branch. We evaluate DIR-BHRNet on the well-known COCO and CrowdPose datasets. The results show that DIR-BHRNet outperforms the state-of-the-art methods in terms of accuracy with a real-time computational cost. Finally, we implement the DIR-BHRNet on the current mainstream Android smartphones, which perform more than 10 FPS. The free-used executable file (Android 10), source code, and a video description of this work are publicly available on the page 1 to facilitate the development of real-time MPPE on smartphones. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13777 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DIR-BHRNet: A Lightweight Network for Real-time Vision-based Multi-person Pose Estimation on Smartphones Lan, Gongjin Wu, Yu Hao, Qi Computer Vision and Pattern Recognition Human pose estimation (HPE), particularly multi-person pose estimation (MPPE), has been applied in many domains such as human-machine systems. However, the current MPPE methods generally run on powerful GPU systems and take a lot of computational costs. Real-time MPPE on mobile devices with low-performance computing is a challenging task. In this paper, we propose a lightweight neural network, DIR-BHRNet, for real-time MPPE on smartphones. In DIR-BHRNet, we design a novel lightweight convolutional module, Dense Inverted Residual (DIR), to improve accuracy by adding a depthwise convolution and a shortcut connection into the well-known Inverted Residual, and a novel efficient neural network structure, Balanced HRNet (BHRNet), to reduce computational costs by reconfiguring the proper number of convolutional blocks on each branch. We evaluate DIR-BHRNet on the well-known COCO and CrowdPose datasets. The results show that DIR-BHRNet outperforms the state-of-the-art methods in terms of accuracy with a real-time computational cost. Finally, we implement the DIR-BHRNet on the current mainstream Android smartphones, which perform more than 10 FPS. The free-used executable file (Android 10), source code, and a video description of this work are publicly available on the page 1 to facilitate the development of real-time MPPE on smartphones. |
| title | DIR-BHRNet: A Lightweight Network for Real-time Vision-based Multi-person Pose Estimation on Smartphones |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.13777 |