Embedded Motion Detection via Neural Response Mixture Background Modeling

Mohammad Javad Shafiee, Parthipan Siva, Paul Fieguth, Alexander Wong; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 19-26

Recent studies have shown that deep neural networks (DNNs) can outperform state-of-the-art 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 performance 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 for constructing Gaussian mixture models to detect moving objects in a scene. NeRM was implemented on an embedded system on an Axis surveillance camera, and results demonstrated that the proposed NeRM approach can strong motion detection accuracy while operating at near real-time performance.