Dr. James K. Starling is an Associate Professor and Director for the Center for Data Analysis and Statistics at the United States Military Academy, West Point. He has served in the United States Army as an Artilleryman and an Operations Research and Systems Analysis (ORSA) analyst for over 23 years. His research interests include military simulations, optimization, remote sensing, and object detection and recognition.
The US Government has a specific need for tools that intelligence analysts can use to search and filter data effectively. Artificial Intelligence (AI), through the application of Deep Neural Networks (DNNs) can assist in a multitude of military applications, requiring a constant supply of relevant data sets to keep up with the always-evolving battlefield. Existing imagery does not adequately represent the evolving nature of modern warfare; therefore, finding a way to simulate images of future conflicts could give us a strategic advantage against our adversaries. Additionally, using physical cameras to capture sufficient various lighting and environmental conditions is nearly impossible. The technical challenge in this area is to create software tools for edge computing devices integrated with cameras to process the video feed locally without having to send the video data through bandwidth-constrained networks to servers in data centers. The ability to collect and process data locally, often in austere environments, can accelerate decision making and action taken in response to emergency situations. An important part of this challenge is to create labeled datasets that are relevant to the problem and are needed for training the edge-efficient AI. Teams from Fayetteville State University (FSU) and The United States Military Academy (USMA) will present their proposed workflows that will enable accurate detection of various threats using Unreal Engine (UE) to generate synthetic training data. In principle, production of synthetic data is unlimited and can be customized to location, various environmental variables, and human and crowd characteristics. Together, both teams address the challenges of realism and fidelity; diversity and variability; and integration with real data.
The focus of the FSU team is on creating semi-automated workflows to create simulated human-crowd behaviors and the ability to detect anomalous behaviors. It will provide methods of specifying collective behaviors to create crowd simulations of many human agents, and for selecting a few of those agents to exhibit behaviors that are outside of the defined range of normality. The analysis is needed for rapid detection of anomalous activities that can pose security threats and cost human lives.
The focus of the USMA team will be in creating semi-autonomous workflows that evaluate the ability of DNNs to identify key military assets under various environmental conditions, specifically armored vehicles and personnel. We aim to vary environmental parameters to simulate varying light conditions and introduce obscuration experiments using artificial means like smoke and natural phenomena like fog to add complexity to the scenarios. Additionally, the USMA team will explore a variety of camouflage patterns and various levels of defilade.
The outcome of both teams is to provide workflow solutions that maximize the use of UE to provide realistic datasets that simulate future battlefields and emergency scenarios for evaluating and training existing models. These studies pave the way for creating advanced models trained specifically for military application. Creating adaptive models that can keep up with today’s evolving battlefield will give the military a great advantage in the race for artificial intelligence applications.
Session Materials: dataworks.testscience.org/wp-...
21 май 2024
