洗浴场景下的去雾人体关键点检测研究
Research on Key Point Detection of Defogging Human Body in Bathing Scene
DOI: 10.12677/sea.2025.143060, PDF,    科研立项经费支持
作者: 任翌霏, 许 朋, 吴伟铭, 喻洪流*：上海理工大学健康科学与工程学院，上海
关键词: 深度学习；人体关键点检测；去雾算法；Deep Learning； Human Keypoint Detection； De-Fogging Algorithm
摘要: 在机器人辅助老年人淋浴的环境中，水雾的存在会掩盖关键身体点的视线，对精确人体关键点检测构成挑战。本研究提出了一种端到端模型，将人类关键点检测与去雾技术相结合。该模型旨在监测老年人在淋浴过程中的关键点，从而防止事故发生。为了评估我们方法的有效性，我们制作了一个具有细水雾的模拟数据集，并比较了各种模型。实验结果表明，与我们精选数据集上的其他模型相比，我们提出的模型实现了82.63%的平均精度均值(mAP)的显著提高，比基线提高了23%。此外，它还可以有效监测老年人的淋浴过程。这项研究具有实际意义，并有可能提高老年人的生活质量。
Abstract: In the context of robot-assisted elderly showering, the presence of water mist can obscure the view of critical body points, posing a challenge for accurate human keypoint detection. This study proposes an end-to-end model that integrates human critical point detection with dehazing techniques. The model aims to monitor critical points in elderly individuals during the showering process, thereby preventing accidents. To assess the effectiveness of our approach, we curated a simulated dataset with featuring fine water mist and compared various models. Experimental results demonstrate that our proposed model achieves a significantly improved mean Average Precision (mAP) of 82.63% compared to other models on our curated dataset, marking increased by 23%. Additionally, it effectively monitors the showering process of the elderly. This research holds practical implications and has the potential to enhance the quality of life for older adults.
文章引用：任翌霏, 许朋, 吴伟铭, 喻洪流. 洗浴场景下的去雾人体关键点检测研究[J]. 软件工程与应用, 2025, 14(3): 682-693. https://doi.org/10.12677/sea.2025.143060

参考文献

[1] Fong, J.H. and Feng, J. (2018) Comparing the Loss of Functional Independence of Older Adults in the U.S. and China. Archives of Gerontology and Geriatrics, 74, 123-127. [Google Scholar] [CrossRef] [PubMed]
[2] Zheng, C., Wu, W., Chen, C., Yang, T., Zhu, S., Shen, J., et al. (2023) Deep Learning-Based Human Pose Estimation: A Survey. ACM Computing Surveys, 56, 1-37. [Google Scholar] [CrossRef] 
[3] Toshev, A. and Szegedy, C. (2014) DeepPose: Human Pose Estimation via Deep Neural Networks. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 23-28 June 2014, 1653-1660. [Google Scholar] [CrossRef] 
[4] Newell, A., Yang, K. and Deng, J. (2016) Stacked Hourglass Networks for Human Pose Estimation. Computer Vision—ECCV 2016, Amsterdam, 11-14 October 2016, 483-499. [Google Scholar] [CrossRef] 
[5] Sun, K., Xiao, B., Liu, D. and Wang, J. (2019) Deep High-Resolution Representation Learning for Human Pose Estimation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, 15-20 June 2019, 5693-5703. [Google Scholar] [CrossRef] 
[6] Cao, Z., Simon, T., Wei, S. and Sheikh, Y. (2017) Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 21-26 July 2017, 7291-7299. [Google Scholar] [CrossRef] 
[7] Wei, S., Ramakrishna, V., Kanade, T. and Sheikh, Y. (2016) Convolutional Pose Machines. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 27-30 June 2016, 4724-4732. [Google Scholar] [CrossRef] 
[8] Tompson, J., Goroshin, R., Jain, A., LeCun, Y. and Bregler, C. (2015) Efficient Object Localization Using Convolutional Networks. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, 7-12 June 2015, 648-656. [Google Scholar] [CrossRef] 
[9] He, K., Gkioxari, G., Dollar, P. and Girshick, R. (2017) Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, 22-29 October 2017, 2961-2969. [Google Scholar] [CrossRef] 
[10] Wang, J., Tan, S., Zhen, X., Xu, S., Zheng, F., He, Z., et al. (2021) Deep 3D Human Pose Estimation: A Review. Computer Vision and Image Understanding, 210, Article ID: 103225. [Google Scholar] [CrossRef] 
[11] He, K.M., Sun, J. and Tang, X.O. (2011) Single Image Haze Removal Using Dark Channel Prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 2341-2353. [Google Scholar] [CrossRef] [PubMed]
[12] Tang, K., Yang, J. and Wang, J. (2014) Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 23-28 June 2014, 2995-3000. [Google Scholar] [CrossRef] 
[13] Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X. and Yang, M. (2016) Single Image Dehazing via Multi-Scale Convolutional Neural Networks. Computer Vision—ECCV 2016, Amsterdam, 11-14 October 2016, 154-169. [Google Scholar] [CrossRef] 
[14] Li, B., Peng, X., Wang, Z., Xu, J. and Feng, D. (2017) An All-in-One Network for Dehazing and Beyond.
[15] Liu, X., Ma, Y., Shi, Z. and Chen, J. (2019) GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, 27 October-2 November 2019, 7314-7323. [Google Scholar] [CrossRef] 
[16] Zhu, J., Park, T., Isola, P. and Efros, A.A. (2017) Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, 22-29 October 2017, 2223-2232. [Google Scholar] [CrossRef] 
[17] Hassan, H., Mishra, P., Ahmad, M., Bashir, A.K., Huang, B. and Luo, B. (2022) Effects of Haze and Dehazing on Deep Learning-Based Vision Models. Applied Intelligence, 52, 16334-16352. [Google Scholar] [CrossRef] 
[18] Liu, D., Wen, B., Liu, X., Wang, Z. and Huang, T. (2018) When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, 13-19 July 2018, 842-848. [Google Scholar] [CrossRef] 
[19] McCartney, E.J. (1976) Optics of the Atmosphere: Scattering by Molecules and Particles.
[20] Narasimhan, S.G. and Nayar, S.K. (2000) Chromatic Framework for Vision in Bad Weather. Proceedings IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, 15 June 2000, 598-605. [Google Scholar] [CrossRef] 
[21] Silberman, N., Hoiem, D., Kohli, P. and Fergus, R. (2012) Indoor Segmentation and Support Inference from RGBD Images. Computer Vision—ECCV 2012, Florence, 7-13 October 2012, 746-760. [Google Scholar] [CrossRef] 
[22] Lin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., et al. (2014) Microsoft COCO: Common Objects in Context. Computer Vision—ECCV 2014, Zurich, 6-12 September 2014, 740-755. [Google Scholar] [CrossRef] 

为你推荐