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Abstract
Estimating human posture is a key element of behavior analysis and human activity recognition (HAR) in many applications, such as public surveillance and gaming. Existing contactless human pose estimation (HPE) methods are mostly vision-based, which may violate privacy and lose functionality in harsh weather and poor light conditions. On the other hand, while being robust against these limitations, mm-wave radars provide high-resolution range data but suffer from no/poor angular resolution. In this article, we employ mm-wave radar along with the inverse synthetic aperture radar (ISAR) algorithm to obtain a high-resolution radar image of a moving person in both range and cross-range dimensions and use the binarized ISAR image as input to an HPE model. The HPE model is trained using labels generated by a vision-based HPE model (AlphaPose). We show that the proposed pipeline can estimate pose from afar (e.g., 4–12 m) using real-world data. We present the pipeline in a general case of a multiple-input-multiple-output (MIMO) radar; however, it can work using a single-input-single-output (SISO) radar as well, providing an extremely affordable solution for behavior analysis applications.
Original language | English |
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Pages (from-to) | 28324-28337 |
Number of pages | 14 |
Journal | IEEE Sensors Journal |
Volume | 24 |
Issue number | 17 |
DOIs | |
Publication status | Published - 16 Jul 2024 |
Bibliographical note
Funding Information:Manuscript received XYZ. The research leading to these results has received funding from IMEC.ICON and Flanders Innovation & Entrepreneurship (nr HBC.2020.3106) \u2013 Project Surv-AI-llance.
Funding Information:
The research leading to these results has received funding from IMEC.ICON and Flanders Innovation & Entrepreneurship (nr HBC.2020.3106) \u2013 Project Surv-AI-llance.
Publisher Copyright:
© 2001-2012 IEEE.
Keywords
- Human Pose Estimation
- Inverse Synthetic Aperture Radar
- Convolutional Neural Network
Projects
- 2 Active
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IOF3016: GEAR: “Venturing into Future Health Technologies”
Stiens, J., Wambacq, P., da Silva Gomes, B. T., Sahli, H., Vandemeulebroucke, J., Jansen, B., Lemeire, J., Steenhaut, K., Munteanu, A., Deligiannis, N., Schelkens, P., Kuijk, M., Parvais, B., Chan, C. W., Van Schependom, J., Touhafi, A., Braeken, A., Runacres, M., Cornelis, B., Schretter, C., Blinder, D., Temmermans, F., Thielemans, S., Papavasileiou, E., Brahimetaj, R., De Canck, H. & Ersu, B.
1/01/21 → 24/12/25
Project: Applied