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Embedded Motion Detection via Neural Response Mixture Background Modeling
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Recent studies have shown that deep neural networks (DNNs) can outperform state-of-the-art algorithms for a multitude of computer vision tasks. However, the ability to leverage DNNs for near real-time performance on embedded systems have been all but impossible so far without requiring specialized processors or GPUs. In this paper, we present a new motion detection algorithm that leverages the power of DNNs while maintaining low computational complexity needed for near real-time embedded
doi:10.1109/cvprw.2016.109
dblp:conf/cvpr/ShafieeSFW16
fatcat:qlb4tvtrffcadjiu34dxsnf6hq