Deep neural networks (DNNs) have shown significant promise in different fields including computer vision. Although previous research have demonstrated the ability of DNNs, utilizing these types of networks for real-time applications on embedded systems is not possible without requiring specialized hardware such as GPUs. In this paper, we propose a new approach to real-time motion detection in videos that leverages the power of DNNs while maintaining low computational complexity needed for real-time performance on existing embedded platforms without specialized hardware. The rich deep features extracted from the neural responses of an efficient, stochastically-formed deep neural network (StochasticNet) are utilized for constructing Gaussian mixture models (GMM) to detect motion in a scene. The proposed Ne ural R esponse M ixture (NeRM) model was embedded on an Axis surveillance camera, and results demonstrate that the proposed NeRM approach can improve the GMM performance (i.e., with less false detection and noise) in modeling the foreground and background compared to other state-of-the-art approaches applied for motion detection on embedded systems while operating at real-time performance.
Journal of Signal Processing Systems – Springer Journals
Published: Jul 13, 2017
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