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Fall Detection from Human Shape and Motion History Using Video Surveillance
Abstract
Nowadays, Western countries have to face the growing population of seniors. New technologies can help people stay at home by providing a secure environment and improving their quality of life. The use of computer vision systems offers a new promising solution to analyze people behavior and detect some unusual events. In this paper, we propose a new method to detect falls, which are one of the greatest risk for seniors living alone. Our approach is based on a combination of motion history and human shape variation. Our algorithm provides promising results on video sequences of daily activities and simulated falls.
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Cited by
A survey on fall detection: Principles and approaches
Mubashir, M., Shao, L., & Seed, L. (2013) · Neurocomputing · doi:10.1016/j.neucom.2011.09.037 · Cited by 869
Robust Video Surveillance for Fall Detection Based on Human Shape Deformation
Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J. (2011) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2011.2129370 · Cited by 490
Self-citationSurvey on Fall Detection and Fall Prevention Using Wearable and External Sensors
Delahoz, Y., & Labrador, M. (2014) · Sensors · doi:10.3390/s141019806 · Cited by 329
Auvinet, E., Multon, F., Saint-Arnaud, A., Rousseau, J., & Meunier, J. (2011) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2010.2087385 · Cited by 256
Self-citationAmbient Sensors for Elderly Care and Independent Living: A Survey
Uddin, M. Z., Khaksar, W., & Torresen, J. (2018) · Sensors · doi:10.3390/s18072027 · Cited by 166
Metadata
- Authors
- Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J.
- Venue
- 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07)
- Publisher
- IEEE
- Published
- 2007
- Pages
- 875-880
- OpenAlex
- W2118218997
- Cited by
- 324
- References
- 0
- FWCI
- 12.04
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Fall Detection from Human Shape and Motion History Using Video Surveillance. https://app.cochat.ai/papers/10.1109/ainaw.2007.181
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- Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J. (2007). Fall Detection from Human Shape and Motion History Using Video Surveillance. 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07). https://doi.org/10.1109/ainaw.2007.181
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
