Abstract
In this paper we investigate the effects of fusing feature streams extracted from color and depth videos, aiming to monitor the actions of people in an assistive environment. The output of fused time-series classifiers is used to model and extract actions. To this end we compare the Hidden Markov model classifier and fusion methods like early, late or state fusion. Our experiments employ a public dataset, which was acquired indoors.
An erratum for this chapter can be found at http://dx.doi.org/10.1007/978-3-642-39351-8_56
Chapter PDF
Similar content being viewed by others
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.
References
Stanford, V.: Using pervasive computing to deliver elder care. IEEE Pervasive Computing 1(1), 10–13 (2002)
Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. 104(2), 90–126 (2006)
Doukas, C., Maglogiannis, I.: Emergency fall incidents detection in assisted living environments utilizing motion, sound, and visual perceptual components. IEEE Transactions on Inf. Techn. in Biomedicine 15(2), 277–289 (2011)
Antonakaki, P., Kosmopoulos, D., Perantonis, S.J.: Detecting abnormal human behaviour using multiple cameras. Signal Processing 89, 1723–1738 (2009)
Kosmopoulos, D.: Multiview behavior monitoring for assistive environments. Universal Access in the Information Society 10, 115–123 (2011)
Christodoulidis, A., Delibasis, K.K., Maglogiannis, I.: Near real-time human silhouette and movement detection in indoor environments using fixed cameras. In: PETRA 2012, pp. 1:1–1:7. ACM (2012)
Ni, B., Wang, G., Moulin, P.: Rgbd-hudaact: A color-depth video database for human daily activity recognition. In: ICCV Workshops, pp. 1147–1153 (2011)
Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3d points. In: CVPR4HB 2010, pp. 9–14 (2010)
Sung, J., Ponce, C., Selman, B., Saxena, A.: Unstructured human activity detection from rgbd images. In: Int. Conf. Robotics and Automation, pp. 842–849 (2012)
Zhao, Y., Liu, Z., Yang, L., Cheng, H.: Combing rgb and depth map features for human activity recognition. In: 2012 Asia-Pacific Signal Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1–4 (December 2012)
Chang, C.C., Lin, C.J.: Libsvm: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011)
Kosmopoulos, D., Doulamis, N., Voulodimos, A.: Bayesian filter based behavior recognition in workflows allowing for user feedback. Computer Vision and Image Understanding 116, 422–434 (2012)
Davis, J.W., Bobick, A.F.: The representation and recognition of action using temporal templates. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 928–934 (1997)
Xiang, T., Gong, S.: Beyond tracking: Modelling activity and understanding behaviour. Int. J. Comput. Vision 67(1), 21–51 (2006)
Chenand, C., Liang, J., Zhao, H., Hu, H., Tian, J.: Factorial HMM and parallel HMM for gait recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 39(1), 114–123 (2009)
Zeng, Z., Tu, J., Pianfetti, B., Huang, T.: Audio-visual affective expression recognition through multistream fused HMM. IEEE Transactions on Multimedia 10(4), 570–577 (2008)
Kosmopoulos, D., Chatzis, S.: Robust visual behavior recognition. IEEE Signal Processing Magazine 27(5), 34–45 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kosmopoulos, D.I., Doliotis, P., Athitsos, V., Maglogiannis, I. (2013). Fusion of Color and Depth Video for Human Behavior Recognition in an Assistive Environment. In: Streitz, N., Stephanidis, C. (eds) Distributed, Ambient, and Pervasive Interactions. DAPI 2013. Lecture Notes in Computer Science, vol 8028. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39351-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-39351-8_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39350-1
Online ISBN: 978-3-642-39351-8
eBook Packages: Computer ScienceComputer Science (R0)