Abstract
The fall events in crowded places are prone to public safety problems, where real-time monitoring and early warning of falls can reduce the safety risks. Aiming at the problems of large scale and poor timeliness of existing fall detection methods based on pose estimation, an OpenPose human fall detection algorithm called DSC-OpenPose is proposed, which incorporates an attention mechanism. Using DenseNet dense connection idea as reference, each layer is directly connected to all previous layers in the channel dimension to achieve feature reuse and reduce the size of model parameters. In order to get the spatial direction dependency and precise location information of the feature map and to increase the pose estimation accuracy, the coordinate attention method is introduced between various stages. The method is proposed to identify fall behavior based on human outer ellipse parameters, head height and lower limb height together to achieve fall detection of human targets. It is showed that the algorithm achieves a good balance between model size and accuracy on the COCO dataset. The fall detection approach simultaneously achieves 98% accuracy and 96.5% precision on the RF dataset, reaching a detection speed of 20.1 frames/s. The model is small enough to support the real-time inference requirements of embedded devices.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kang, Y., Kang, H., Kim, J.: Fall detection method based on pose estimation using GRU. In: Lee, R., Kim, J.B. (eds.) SNPD 2021. SCI, vol. 951, pp. 169–179. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67008-5_14
Chen, W., Jiang, Z., Guo, H., et al.: Fall detection based on key points of human-skeleton using openpose. Symmetry 12(5), 744 (2020)
Nogas, J., Khan, S.S., Mihailidis, A.: DeepFall: non-invasive fall detection with deep spatio-temporal convolutional autoencoders. J. Healthc. Inform. Res. 4(1), 50–70 (2020)
Cai, Y., et al.: Learning delicate local representations for multi-person pose estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 455–472. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58580-8_27
Xu, Y., Zhang, J., Zhang, Q., et al.: ViTPose: simple vision transformer baselines for human pose estimation. arXiv preprint arXiv:2204.12484 (2022)
Pishchulin, L., Insafutdinov, E., Tang, S., et al.: Deepcut: joint subset partition and labeling for multi person pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4929–4937 (2016)
Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, Part VIII, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29
Kreiss, S., Bertoni, L., Alahi, A.: PifPaf: composite fields for human pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11977–11986 (2019)
Cheng, B., Xiao, B., Wang, J., et al.: HigherHRNet: scale-aware representation learning for bottom-up human pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5386–5395 (2020)
Cao, Z., Hidalgo, G., Simon, T., et al.: OpenPose: realtime multi-person 2D pose estimation using part affinity fields. IEEE Trans. Pattern Anal. Mach. Intell. 43(1), 172–186 (2021)
Huang, G., Liu, Z., Van Der Maaten, L., et al.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Osokin, D.: Real-time 2D multi-person pose estimation on CPU: lightweight openpose. arXiv preprint arXiv:1811.12004 (2019)
Chen, Z., Wang, Y., Yang, W.: Video based fall detection using human poses. In: Liao, X., et al. (eds.) BigData 2022. CCIS, vol. 1496, pp. 283–296. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-9709-8_19
Menacho, C., Ordoñez, J.: Fall detection based on CNN models implemented on a mobile robot. In: 2020 17th International Conference on Ubiquitous Robots (UR), pp. 284–289. IEEE (2020)
Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713–13722 (2021)
Liu, Q., Kortylewski, A., Yuille, A.: PoseExaminer: automated testing of out-of-distribution robustness in human pose and shape estimation. arXiv preprint arXiv:2303.07337 (2023)
Yang, J., Zeng, A., Liu, S., et al.: Explicit box detection unifies end-to-end multi-person pose estimation. arXiv preprint arXiv:2302.01593 (2023)
Acknowledgement
It is supported by National Key R&D Program of China (grant no. 2018YFB1701401, 2020YFB1712401-1), National Natural Science Foundation of China (grant no. 62006210, 62001284), Key Project of Public Benefit in Henan Province of China (grant no. 201300210500), Science and technology public relations project of Henan Province (grant no. 212102210098, 202102210373) and the Research Foundation for Advanced Talents of Zhengzhou University (grant no. 32340306).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shi, L., Xue, H., Meng, C., Gao, Y., Wei, L. (2023). DSC-OpenPose: A Fall Detection Algorithm Based on Posture Estimation Model. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14090. Springer, Singapore. https://doi.org/10.1007/978-981-99-4761-4_23
Download citation
DOI: https://doi.org/10.1007/978-981-99-4761-4_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4760-7
Online ISBN: 978-981-99-4761-4
eBook Packages: Computer ScienceComputer Science (R0)