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.
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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
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DOI: https://doi.org/10.1007/978-3-319-13105-4_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13104-7
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