A NOVEL NETWORK BASED ON ATTENTION FOR FALLING DETECTION
DOI:
https://doi.org/10.51453/2354-1431/2023/960Keywords:
Falling, Detection, Action, Recognition, Attention.Abstract
The attention mechanism has been studied extensively in recent years to enhance the model’s learning ability. In this paper, we propose a new attention mechanism including the temporal attention module and spatial attention module. These two modules are combined in the 3D ResNet-18 network to provide "attention" to the critical features of the volume. In particular, the temporal attention module exploits the motion relationship between frames, and the spatial attention module is interested in the spatial relationship between features. The experimental results for the proposed model show that our proposed method achieves competitive performance compared with the recently published modern deep and heavy networks.
Downloads
References
[1] P. T. T. Trang, M. T. H. Thu, “MỘT MÔ HÌNH HỌC SÂU CHO BÀI TOÁN PHÁT HIỆN NGƯỜI BỊ NGÃ,” TNU Journal of Science and Technology, 225(14), 48-53, 2020.
[2] T. C. Án, L. M. Phúc, Đ. T. Đức, N. B. Hùng, L. Đ. Chiến, P. T. X. Diễm, S. B. Pha, “Phát hiện té ngã cho người cao tuổi bằng gia tốc kế và mô hình học sâu Long Short-Term Memory.” Tạp chí Khoa học Trường Đại học Cần Thơ, (CĐ Công nghệ TT 2017), 65-71, 2017.
[3] S. A. Carneiro, G. P. da Silva, G. V. Leite, R. Moreno, S. J. F. Guimarães, H. Pedrini, “Multi-stream deep convolutional network using high-level features ap¬plied to fall detection in video sequences,” in 2019 International Conference on Systems, Signals and Im¬age Processing (IWSSIP), 293-298, IEEE, 2019.
[4] Y. Chen, X. Kong, L. Meng, H. Tomiyama, “An edge computing based fall detection system for elderly per¬sons,” Procedia Computer Science, 174, 9-14, 2020.
[5] D.-Q. Vu, N. T. Le, J.-C. Wang, “(2+ 1) D Distilled ShuffleNet: A Lightweight Unsupervised Distillation Network for Human Action Recognition,” in 2022 26th International Conference on Pattern Recognition (ICPR), 3197-3203, IEEE, 2022.
[6] J. Gou, B. Yu, S. J. Maybank, D. Tao, “Knowledge distillation: A survey,” International Journal of Com¬puter Vision, 129, 1789-1819, 2021.
[7] D.-Q. Vu, N. Le, J.-C. Wang, “Teaching yourself: A self-knowledge distillation approach to action recog¬nition,” IEEE Access, 9, 105711-105723, 2021.
[8] D.-Q. Vu, J.-C. Wang, et al., “A novel self-knowledge distillation approach with siamese representation learning for action recognition,” in 2021 International Conference on Visual Communications and Image Processing (VCIP), 1-5, IEEE, 2021.
[9] Q. V. Duc, T. Phung, M. Nguyen, B. Y. Nguyen, T. H. Nguyen, “Self-knowledge Distillation: An Efficient Approach for Falling Detection,” in Artificial Intelli¬gence in Data and Big Data Processing: Proceedings of ICABDE 2021, 369-380, Springer, 2022.
[10] B.-H. Wang, J. Yu, K. Wang, X.-Y. Bao, K.-M. Mao, “Fall detection based on dual-channel feature integra¬tion,” IEEE Access, 8, 103443-103453, 2020.
[11] M. M. Hasan, M. S. Islam, S. Abdullah, “Robust pose¬based human fall detection using recurrent neural net¬work,” RAAICON, 48-51, 2019.
[12] Z. Cao, T. Simon, S.-E. Wei, Y. Sheikh, “Realtime multi-person 2d pose estimation using part affinity fields,” CVPR, 7291-7299, 2017.
[13] X. Cai, S. Li, X. Liu, G. Han, “Vision-based fall de¬tection with multi-task hourglass convolutional auto¬encoder,” IEEE Access, 8, 44493-44502, 2020.
[14] K. He, X. Zhang, S. Ren, J. Sun, “Deep residual learn¬ing for image recognition,” in CVPR, 770-778, 2016.
[15] K. Hara, H. Kataoka, Y. Satoh, “Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?” in CVPR, 6546-6555, 2018.
[16] J. Hu, L. Shen, G. Sun, “Squeeze-and-excitation net¬works,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 7132-7141, 2018.
[17] S. Woo, J. Park, J.-Y. Lee, I. S. Kweon, “Cbam: Con¬volutional block attention module,” in Proceedings of the European conference on computer vision (ECCV), 3-19, 2018.
[18] T. Phung, V. T. Nguyen, T. H. T. Ma, Q. V. Duc, “A (2+ 1) D attention convolutional neural network for video prediction,” in Artificial Intelligence in Data and Big Data Processing: Proceedings of ICABDE 2021, 395¬406, Springer, 2022.
[19] B. Kwolek, M. Kepski, “Human fall detection on embedded platform using depth maps and wireless accelerometer,” Computer methods and programs in biomedicine, 117(3), 489-501, 2014.
[20] A. Nunez-Marcos, G. Azkune, I. Arganda-Carreras, “Vision-based fall detection with convolutional neu¬ral networks,” Wireless communications and mobile computing, 2017, 2017.
[21] T. X. Hoa, V. R. M. Madrid, E. A. Albacea, “A Lightweight Model for Falling Detection,” RIVF,
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All articles published in SJTTU are licensed under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA) license. This means anyone is free to copy, transform, or redistribute articles for any lawful purpose in any medium, provided they give appropriate attribution to the original author(s) and SJTTU, link to the license, indicate if changes were made, and redistribute any derivative work under the same license.
Copyright on articles is retained by the respective author(s), without restrictions. A non-exclusive license is granted to SJTTU to publish the article and identify itself as its original publisher, along with the commercial right to include the article in a hardcopy issue for sale to libraries and individuals.
Although the conditions of the CC BY-SA license don't apply to authors (as the copyright holder of your article, you have no restrictions on your rights), by submitting to SJTTU, authors recognize the rights of readers, and must grant any third party the right to use their article to the extent provided by the license.
