Monocular Camera Fall Detection System Exploiting 3D Measures: A Semi-supervised Learning Approach
Abstract
Falls have been reported as the leading cause of injury-related visits to emergency departments and the primary etiology of accidental deaths in elderly. The system presented in this article addresses the fall detection problem through visual cues. The proposed methodology utilize a fast, real-time background subtraction algorithm based on motion information in the scene and capable to operate properly in dynamically changing visual conditions, in order to detect the foreground object and, at the same time, it exploits 3D space’s measures, through automatic camera calibration, to increase the robustness of fall detection algorithm which is based on semi-supervised learning. The above system uses a single monocular camera and is characterized by minimal computational cost and memory requirements that make it suitable for real-time large scale implementations.
Keywords
image motion analysis semisupervised learning self calibration fall detectionPreview
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References
- 1.Le, T., Pan, R.: Accelerometer-based sensor network for fall detection. In: Biomedical Circuits and Systems Conference, BioCAS, pp. 265–268. IEEE (November 2009)Google Scholar
- 2.Nyan, M., Tay, F.E., Murugasu, E.: A wearable system for pre-impact fall detection. Journal of Biomechanics 41(16), 3475–3481 (2008)CrossRefGoogle Scholar
- 3.Bianchi, F., Redmond, S., Narayanan, M., Cerutti, S., Lovell, N.: Barometric pressure and triaxial Accelerometry-Based falls event detection. IEEE Trans. on Neural Systems and Rehabilitation Engineering 18(6), 619–627 (2010)CrossRefGoogle Scholar
- 4.Zigel, Y., Litvak, D., Gannot, I.: A method for automatic fall detection of elderly people using floor vibrations and sound - proof of concept on human mimicking doll falls. IEEE Trans. on Biomedical Eng. 56(12), 2858–2867 (2009)CrossRefGoogle Scholar
- 5.Debard, G., Karsmakers, P., Deschodt, M., Vlaeyen, E., Van den Bergh, J., Dejaeger, E., Milisen, K., Goedemé, T., Tuytelaars, T., Vanrumste, B.: Camera based fall detection using multiple features validated with real life video (July 2011)Google Scholar
- 6.Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Robust video surveillance for fall detection based on human shape deformation. IEEE Trans. on CSVT (5), 611–622 (2011)Google Scholar
- 7.Fu, Z., Culurciello, E., Lichtsteiner, P., Delbruck, T.: Fall detection using an address-event temporal contrast vision sensor. In: ISCAS 2008, pp. 424–427 (2008)Google Scholar
- 8.Foroughi, H., Rezvanian, A., Paziraee, A.: Robust fall detection using human shape and multi-class support vector machine. In: ICVGIP 2008, pp. 413–420 (2008)Google Scholar
- 9.Diraco, G., Leone, A., Siciliano, P.: An active vision system for fall detection and posture recognition in elderly healthcare. In: DATE 2010, pp. 1536–1541 (2010)Google Scholar
- 10.Thome, N., Miguet, S., Ambellouis, S.: A Real-Time, multiview fall detection system: A LHMM-Based approach. IEEE Trans. on CSVT 18(11), 1522–1532 (2008)Google Scholar
- 11.Doulamis, N.: Iterative motion estimation constrained by time and shape for detecting person’s falls. In: ACM 3rd Inter. Conference on Pervasive Technologies Related to Assistive Environments, Samos, GreeceGoogle Scholar
- 12.Qian, H., Mao, Y., Xiang, W., Wang, Z.: Home environment fall detection system based on a cascaded multi-SVM classifier. In: ICARCV 2008, pp. 1567–1572 (2008)Google Scholar
- 13.Grammatikopoulos, L., Karras, G., Petsa, E.: An automatic approach for camera calibration from vanishing points. ISPRS 62(1), 64–76 (2007)CrossRefGoogle Scholar
- 14.Bevilacqua, A., Gherardi, A., Carozza, L.: Automatic perspective camera calibration based on an incomplete set of chessboard markers. In: Sixth ICVGIP, Bhubaneswar, India, pp. 126–133 (2008)Google Scholar