References
- N. Thome, S. Miguet, and S. Ambellous, “A Real-Time, Multiview Fall Detection System: A LHMM-Based Approach,” IEEE Trans. Circuits and Systems for Video Technology, vol. 18, no. 11, 2008, pp. 1522-1532.
- L. C. D. Silva, “Audiovisual Sensing Of Human Movements For Home-Care And Security In A Smart Environment,” International Journal on Smart Sensing and Intelligent Systems, vol. 1, no 1, 2008, pp. 220-245.
- H. Foroughi, A. Naseri, A. Saberi, and H. S. Yazdi, “An eigenspace-based approach for elderly fall detection using Integrated Time Motion Image and Neural Network,” Proc. 9th Int’l Conf. Signal Processing (ICSP 2008), IEEE Press, 2008, pp. 1499-1503.
- W. Chia-Chi, C. Chin-Yen, L. Po-Yen, C. Yi-Chieh, I. T. Kuo, H. Chih-Ning, and C. ChiaTai, “Development of a Fall Detecting System for the Elderly Residents,” Proc. 2nd Int’l Conf. Bioinformatics and Biomedical Engineering (ICBBE 2008), 2008. IEEE Press, 2008, pp. 13591362.
- C. Sminchisescu and B. Triggs, “Estimating articulated human motion with covariance scaled sampling,” IJRR, vol. 22, no. 6, pp. 371-391, Jun. 2003.
- G. Mori, “Recovering 3D human body configurations using shape contexts,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 7, pp. 1052-1062, Jul. 2006.
- P. F. Felzenszwalb and D. P. Huttenlocher, “Pictorial structures for object recognition,” Int. J. Comput. Vis., vol. 61, no. 1, pp. 55-79, 2005.
- I. Haritaoglu, D. Harwood, and L. Davis, “W4: Real-time surveillance of people and their activities,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 8, pp. 809-830, Aug. 2000.
- H. Zhong, J. Shi, and M. Visontai, “Detecting unusual activities in video,” Comput. Vis. Pattern Recognit., 2004. [6] J. W. Davis and A. F. Bobick, “The representation and recognition of action using temporal templates,” Comput. Vis. Pattern Recognit., 1997.
- L. R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proc. IEEE, vol. 77, pp. 257-286, 1989.
- N. M. Oliver, B. Rosario, and A. Pentland, “A bayesian computer vision system for modeling human interactions,” Pattern Anal. Mach. Intell., pp. 831-843, 2000.
- R. Cucchiara, H. Rita, A. Prati, O. Andrea, R. Vezzani, and C. Roberto, “A multi-camera vision system for fall detection and alarm generation,” Expert Syst., vol. 24, no. 5, pp. 334-345, Nov. 2007.
- C. Stauffer and W. Grimson, “Learning patterns of activity using realime tracking,” Pattern Anal. Mach. Intell., 2000.
- N. Thome and S. Miguet, “A robust appearance model for tracking human motions,” AVSS, pp. 528-533, 2005.
- M. Kangas, A. Konttila, I. Winblad, and T. Jamsa, “Determination of simple thresholds for accelerometry-based parameters for fall detection,” Proc. 29th Annual Int’l Conf. Engineering in Medicine and Biology Society (EMBS 2007), IEEE CS Press, 2007, pp. 1367-1370.
- D. M. Karantonis, M. R. Narayanan, M. Mathie, N. H. Lovell, and B. G. Celler, “Implementation of a real-time elderly movement classifier using a triaxial accelerometer for ambulatory monitoring,” IEEE Trans. Information Technology in Biomedicine, vol. 10, no. 1, 2006, pp. 156-167.
- J. Y. Lee and K. Ya, “Multi-Target, Multi-Sensor Tracking Based On Quality-Of-Information And Formal Bayesian Frameworks,” International Journal on Smart Sensing and Intelligent Systems, vol. 1, no 4, 2008, pp. 842-857.
- Peter Wide, “Human-Based Sensing – Sensor Systems to Complement Human Perception,” International Journal on Smart Sensing and Intelligent Systems, vol. 1, no 1, 2008, pp. 57-69.