Abstract
This paper presents a Video Monitoring System, which aims to achieve behavior analysis of elderly people. Real-time tracking and posture discrimination enable to detect emergency situation (by trigging an alarm in case of fall detection for example) and to analyze long term activity which enforces medical follow-up. These are key-issues to improve healthcare quality for rural population. Monitoring human activity in a home environment is a challenging task in computer vision. A multi-camera system is proposed to address the complexity of home environment. Person silhouette is extracted thanks to a robust background/foreground segmentation process. A multi-view particle filter is built to track the silhouette in the scene and discriminate the person posture. This posture is used to interpret basic activities and detect falls. A finer gesture reconstruction is finally exposed which will offer a more accurate activity determination and gait analysis for future system.
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References
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real time tracking. In: Proc IEEE CVPR (1999)
Chen, Y.-T., Chen, C.-S., Huang, C.-R., Hung, Y.-P.: Efficient hierarchical method for background subtraction. Pattern Recognition 40(10) (2006)
Huang, C.R., Chen, C.S., Chung, P.C.: Contrast context histogram - a discriminating local descriptor for image matching. Proc IEEE, Pattern recognition, 53–56 (2006)
Osawa, T., Wu, X., Wakabayashi, K., Yasuno, T.: Human tracking by particle filtering using full model of both target and environment. In: Proc IEEE ICPR, vol. 2, pp. 25–28 (2006)
Ristic, B., Arulampalam, S., Gordon, N.: Beyond the Kalman filter: Tracking applications of particle filters. Artech House (2003)
Bezzi, M., Groenevelt, R.: Toward understanding and modeling individual behavior and group dynamics. In: PTA 2006 workshop, Dublin, Ireland, May 7 (2006)
McKenna, S.J., Nait-Charif, H.: Summarizing contextual activity and detection unusual inactivity in a supportive home environment. PAA 7(4), 386–401 (2004)
Bromley, P.A., Courtney, P., Thacker, N.A.: Design of a visual system for detecting natural events by the use of an independent visual estimate: a human fall detector. In: Empirical Evaluation Methods in Computer Vision (2002)
Gond, L., Sayd, P., Chateau, T., Dhome, M.: A 3D Shape Descriptor for human Pose Recovery. In: Conf. on Articulated Motion and Deformable Objects, Mallorca (2008)
Wactlar, H.D., Christel, M., Hauptmann, A., Chen, D., Yang, J.: Infrastructure for Machine Understanding of Video Observations in Skilled Care Facilities – Implications of Early Results from CareMedia Case Studies, UbiHealth (2004)
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Pham, QC., Dhome, Y., Gond, L., Sayd, P. (2008). Video Monitoring of Vulnerable People in Home Environment. In: Helal, S., Mitra, S., Wong, J., Chang, C.K., Mokhtari, M. (eds) Smart Homes and Health Telematics. ICOST 2008. Lecture Notes in Computer Science, vol 5120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69916-3_11
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DOI: https://doi.org/10.1007/978-3-540-69916-3_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69914-9
Online ISBN: 978-3-540-69916-3
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