The Open Standard for Unattended Sensors (OSUS) was developed by DIA and ARL to provide a plug-n-play platform for sensor interoperability. Our objective is to use the standardized data produced by OSUS in performing data analytics on information obtained from various sensors. Data analytics can be integrated in one of three ways: within an asset itself; as an independent plug-in designed for one type of asset (i.e. camera or seismic sensor); or as an independent plug-in designed to incorporate data from multiple assets. As a proof-of-concept, we develop a model that can be used in the second of these types – an independent component for camera images. The dataset used was collected as part of a demonstration and test of OSUS capabilities. The image data includes images of empty outdoor scenes and scenes with human or vehicle activity. We design, test, and train a convolution neural network (CNN) to analyze these images and assess the presence of activity in the image. The resulting classifier labels input images as empty or activity with 86.93% accuracy, demonstrating the promising opportunities for deep learning, machine learning, and predictive analytics as an extension of OSUS’s already robust suite of capabilities.
Kelly Cashion, Darian Landoll, Kevin Klawon, and Nilesh Powar, "Performing data analytics on information obtained from various sensors on an OSUS compliant system," Proc. SPIE 10190, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII, 1019005 (Presented at SPIE Defense + Security: April 10, 2017; Published: 4 May 2017); https://doi.org/10.1117/12.2266578.
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