Embedded Motion Detection via Neural Response Mixture Background Modeling

Mohammad Javad Shafiee, Parthipan Siva, Paul Fieguth, Alexander Wong
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
more » ... ance without specialized hardware. The proposed Neural Response Mixture (NeRM) model leverages rich deep features extracted from the neural responses of an efficient, stochastically-formed deep neural network (Stochas-ticNet) for constructing Gaussian mixture models to detect motion in a scene. NeRM was implemented embedded on an Axis surveillance camera, and results demonstrated that the proposed NeRM approach can achieve strong motion detection accuracy while operating at near real-time performance.
doi:10.1109/cvprw.2016.109 dblp:conf/cvpr/ShafieeSFW16 fatcat:qlb4tvtrffcadjiu34dxsnf6hq