Abstract
We present a system for fall detection in which the fall hypothesis, generated on the basis of accelerometric data, is validated by k-NN based classifier operating on depth features. We show that validation of the alarms in such a way leads to lower ratio of false alarms. We demonstrate the detection performance of the system using publicly available data. We discuss algorithms for person detection in images acquired by both a static and an active depth sensor. The head is modeled in 3D by an ellipsoid that is matched to point clouds, and which is also projected into 2D, where it is matched to edges in the depth maps.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Isard, M., Blake, A.: CONDENSATION - conditional density propagation for visual tracking. Int. J. Computer Vision 29(1), 5–28 (1998)
Kepski, M., Kwolek, B., Austvoll, I.: Fuzzy inference-based reliable fall detection using Kinect and accelerometer. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 266–273. Springer, Heidelberg (2012)
Kepski, M., Kwolek, B.: Unobtrusive fall detection at home using kinect sensor. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013, Part I. LNCS, vol. 8047, pp. 457–464. Springer, Heidelberg (2013)
Kwolek, B.: Face tracking system based on color, stereovision and elliptical shape features. In: IEEE Conf. on Adv. Video and Signal Based Surveill., pp. 21–26 (2003)
Kwolek, B.: Visual system for tracking and interpreting selected human actions. Journal of WSCG 11(2), 274–281 (2003)
Mubashir, M., Shao, L., Seed, L.: A survey on fall detection: Principles and approaches. Neurocomputing 100, 144–152 (2013), special issue: Behaviours in video
Nait-Charif, H., McKenna, S.J.: Activity summarisation and fall detection in a supportive home environment. In: Int. Conf. on Pattern Rec., pp. 4:323–4:326 (2004)
Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Monocular 3D head tracking to detect falls of elderly people. In: 28th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, EMBS 2006, pp. 6384–6387 (2006)
Russakoff, D.B., Herman, M.: Head tracking using stereo. Mach. Vision Appl. 13(3), 164–173 (2002)
Stone, E., Skubic, M.: Fall detection in homes of older adults using the Microsoft Kinect. IEEE J. of Biomedical and Health Informatics (2014)
Vishwakarma, S., Agrawal, A.: A survey on activity recognition and behavior understanding in video surveillance. Vis. Comput. 29, 983–1009 (2013)
Weinland, D., Ronfard, R., Boyer, E.: A survey of vision-based methods for action representation, segmentation and recognition. CVIU 115(2), 224–241 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kępski, M., Kwolek, B. (2014). Person Detection and Head Tracking to Detect Falls in Depth Maps. In: Chmielewski, L.J., Kozera, R., Shin, BS., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2014. Lecture Notes in Computer Science, vol 8671. Springer, Cham. https://doi.org/10.1007/978-3-319-11331-9_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-11331-9_39
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11330-2
Online ISBN: 978-3-319-11331-9
eBook Packages: Computer ScienceComputer Science (R0)