Abstract
Automatic detection of a falling person in video is an important problem with applications in security and safety areas including supportive home environments and CCTV surveillance systems. Human motion in video is modeled using Hidden Markov Models (HMM) in this paper. In addition, the audio track of the video is also used to distinguish a person simply sitting on a floor from a person stumbling and falling. Most video recording systems have the capability of recording audio as well and the impact sound of a falling person is also available as an additional clue. Audio channel data based decision is also reached using HMMs and fused with results of HMMs modeling the video data to reach a final decision.
This work is supported in part by European Commission 6th Framework Program with grant number FP6-507752 (MUSCLE Network of Excellence Project)
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References
Barnes, N.M., Edwards, N.H., Rose, D.A.D., Garner, P.: Lifestyle Monitoring: Technology for Supported Independence. IEE Comp. and Control Eng. J., 169–174 (1998)
Bonner, S.: Assisted Interactive Dwelling House: Edinvar Housing Assoc. Smart Tech. Demonstrator and Evaluation Site. In: Improving the Quality of Life for the European Citizen (TIDE), pp. 396–400 (1997)
McKenna, S.J., Marquis-Faulkes, F., Gregor, P., Newell, A.F.: Scenario-based Drama as a Tool for Investigating User Requirements with Application to Home Monitoring for Elderly People. In: Proc. of HCI (2003)
Nait-Charif, H., McKenna, S.: Activity Summarisation and Fall Detection in a Supportive Home Environment. In: Proc. of ICPR 2004, pp. 323–326 (2004)
Cuntoor, N.P., Yegnanarayana, B., Chellappa, R.: Interpretation of State Sequences in HMM for Activity Representation. In: Proc. of IEEE ICASSP 1905, pp. 709–712 (2005)
Collins, R.T., Lipton, A.J., Kanade, T., Fujiyoshi, H., Duggins, D., Tsin, Y., Tolliver, D., Enomoto, N., Hasegawa, O., Burt, P., Wixson, L.: A System for Video Surveillance and Monitoring: VSAM Final Report.Technical Report CMU-RI-TR-00- 12, Carnegie Mellon University (1998)
Bagci, M., Yardimci, Y., Cetin, A.E.: Moving Object Detection Using Adaptive Subband Decomposition and Fractional Lower Order Statistics in Video Sequences. In: Signal Processing, pp. 1941–1947. Elsevier, Amsterdam (2002)
Stauffer, C., Grimson, W.E.L.: Adaptive Background Mixture Models for Real-Time Tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 246–252 (1999)
Kim, C.W., Ansari, R., Cetin, A.E.: A class of linear-phase regular biorthogonal wavelets. In: Proc. of IEEE ICASSP 1992, pp. 673–676 (1992)
Jabloun, F., Cetin, A.E., Erzin, E.: Teager Energy Based Feature Parameters for Speech Recognition in Car Noise. IEEE Signal Processing Letters, 259–261 (1999)
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Töreyin, B.U., Dedeoğlu, Y., Çetin, A.E. (2005). HMM Based Falling Person Detection Using Both Audio and Video. In: Sebe, N., Lew, M., Huang, T.S. (eds) Computer Vision in Human-Computer Interaction. HCI 2005. Lecture Notes in Computer Science, vol 3766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573425_21
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DOI: https://doi.org/10.1007/11573425_21
Publisher Name: Springer, Berlin, Heidelberg
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