DIR-BHRNet: A Lightweight Network for Real-time Vision-based Multi-person Pose Estimation on Smartphones
<|reference_start|>DIR-BHRNet: A Lightweight Network for Real-time Vision-based Multi-person Pose Estimation on Smartphones: 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.<|reference_end|>
@article{lan2024dir-bhrnet:,
    title={DIR-BHRNet: A Lightweight Network for Real-time Vision-based
  Multi-person Pose Estimation on Smartphones},
    author={Gongjin Lan and Yu Wu and Qi Hao},
    journal={arXiv preprint arXiv:2407.13777},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13777},
    primaryClass={cs.CV}
}