File size: 1,809 Bytes
ae419ed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | \begin{thebibliography}{1}
\providecommand{\url}[1]{\texttt{#1}}
\providecommand{\urlprefix}{URL }
\providecommand{\doi}[1]{https://doi.org/#1}
\bibitem{mcfd}
Auvinet, E., Multon, F., Saint-Arnaud, A., Rousseau, J., Meunier, J.: Fall
detection using body geometry and human motion analysis. In: International
Conference on Aging, Disability and Independence (2011)
\bibitem{chen2021ctrgcn}
Chen, Y., Zhang, Z., Yuan, C., Li, B., Deng, Y., Hu, W.: Channel-wise topology
refinement graph convolution for skeleton-based action recognition. In:
IEEE/CVF International Conference on Computer Vision (2021)
\bibitem{duan2022posec3d}
Duan, H., Zhao, Y., Chen, K., Shao, D., Lin, D., Dai, B.: Revisiting
skeleton-based action recognition. In: IEEE/CVF Conference on Computer Vision
and Pattern Recognition (2022)
\bibitem{urfd}
Kepski, M., Kwolek, B.: Human fall detection on embedded platform using depth
maps and wireless accelerometer. Computer Methods and Programs in Biomedicine
\textbf{117}(3), 489--501 (2014)
\bibitem{lin2017focal}
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense
object detection. IEEE Transactions on Pattern Analysis and Machine
Intelligence \textbf{42}(2), 318--327 (2020)
\bibitem{shi2019agcn}
Shi, L., Zhang, Y., Cheng, J., Lu, H.: Two-stream adaptive graph convolutional
networks for skeleton-based action recognition. In: IEEE/CVF Conference on
Computer Vision and Pattern Recognition (2019)
\bibitem{yolov8}
{Ultralytics}: Ultralytics yolov8.
\url{https://github.com/ultralytics/ultralytics} (2023)
\bibitem{yan2018stgcn}
Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for
skeleton-based action recognition. In: AAAI Conference on Artificial
Intelligence (2018)
\end{thebibliography}
|