Mission Driven Scene Understanding: Candidate Model Training and Validation

Report No. ARL-TN-0781
Authors: Arnold Tunick
Date/Pages: September 2016; 18 pages
Abstract: Army missions take place in dynamic environments, where changing illumination, precipitation, and vegetation can modify saliency and context of an outdoor scene, obscure features, and degrade object recognition. For Army missions, scene understanding tools need to account for dynamic environments that change as a function of space and time and should be tested in mission simulating conditions. In addition, the impact of dynamic environments should be included in the scene understanding approach. At this stage, we are evaluating different computational frameworks that may be useful to incorporate dynamic environments into mission driven scene understanding. One of the candidate engines that we are evaluating is a convolutional neural network (CNN) program installed on a Windows 10 notebook computer. In this report, we present progress toward the proof-of-principle testing of the candidate model to examine the impact of dynamic environments on scene understanding model results.
Distribution: Approved for public release
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Last Update / Reviewed: September 1, 2016