Real-Time Embedded Motion Detection via Neural Response Mixture Modeling

Real-Time Embedded Motion Detection via Neural Response Mixture Modeling 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Signal Processing Systems Springer Journals

Real-Time Embedded Motion Detection via Neural Response Mixture Modeling

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Engineering; Signal,Image and Speech Processing; Circuits and Systems; Electrical Engineering; Image Processing and Computer Vision; Pattern Recognition; Computer Imaging, Vision, Pattern Recognition and Graphics
ISSN
1939-8018
eISSN
1939-8115
D.O.I.
10.1007/s11265-017-1265-3
Publisher site
See Article on Publisher Site

Abstract

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

Journal of Signal Processing SystemsSpringer Journals

Published: Jul 13, 2017

References

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