Abstract
Due to the increasing demand of multi-camera setup and long-term monitoring in vision applications, real-time multi-view action recognition has gain a great interest in recent years. In this paper, we propose a multiple kernel learning based fusion framework that employs a motion-based person detector for finding regions of interest and local descriptors with bag-of-words quantisation for feature representation. The experimental results on a multi-view action dataset suggest that the proposed framework significantly outperforms simple fusion techniques and state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chaaraoui, A.A., Climent-Perez, P., Florez-Revuelta, F.: Silhouette-based human action recognition using sequences of key poses. Pattern Recognition Letters 34, 1799–1807 (2013)
Chaaraoui, A.A., Padilla-Lopez, J.R., Ferrandez-Pastor, F.J., Nieto-Hidalgo, M., Florez-Revuelta, F.: A vision-based system for intelligent monitoring: human behaviour analysis and privacy by context. Sensors 14, 8895–8925 (2014)
Cilla, R., Patricio, M.A., Berlanga, A.: A probabilistic, discriminative and distributed system for the recognition of human actions from multiple views. Neurocomputing 75, 78–87 (2012)
Cilla, R., Patricio, M.A., Berlanga, A., Molina, J.M.: Human action recognition with sparse classification and multiple-view learning. Expert Systems (2013), doi:10.1111/exsy.12040
Gonen, M., Alpaydm, E.: Multiple kernel learning algorithms. Journal of Machine Learning Research (JMLR) 12, 2211–2268 (2011)
Holte, M., Chakraborty, B., Gonzalez, J., Moeslund, T.: A local 3-D motion descriptor for mult-view human action recognition from 4-D spatio-temporal interest points. IEEE Journal of Selected Topics in Signal Processing 6, 553–565 (2012)
Stauffer, C., Grimson, W.: Learning patterns of activity using real time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 22(8), 747–767 (2000)
Varma, M., Babu, B.R.: More generality in efficient multiple kernel learning. In: International Conference on Machine Learning, ICML (2009)
Vedaldi, A., Gulshan, V., Varma, M., Zisserman, A.: Multiple kernels for object detection. In: International Conference on Computer Vision, ICCV (2009)
Wang, H., Schmid, C.: Action recognition with improved trajectories. In: IEEE International Conference on Computer Vision, ICCV (2013)
Weinland, D., Özuysal, M., Fua, P.: Making action recognition robust to occlusions and viewpoint changes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 635–648. Springer, Heidelberg (2010)
Weinland, D., Ronfard, R., Boyer, E.: Free viewpoint action recognition using motion history volumes. Computer Vision and Image Understanding 104(2-3), 249–257 (2006)
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
Gu, F., Flórez-Revuelta, F., Monekosso, D., Remagnino, P. (2014). A Multiple Kernel Learning Based Fusion Framework for Real-Time Multi-View Action Recognition. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds) Ambient Assisted Living and Daily Activities. IWAAL 2014. Lecture Notes in Computer Science, vol 8868. Springer, Cham. https://doi.org/10.1007/978-3-319-13105-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-13105-4_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13104-7
Online ISBN: 978-3-319-13105-4
eBook Packages: Computer ScienceComputer Science (R0)