Mr. John Choi Profile

John Choi

at DCS Corp

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Proceedings Article | 12 April 2021 Poster + Paper

Soldier-led Adaptation of Autonomous Agents (SLA3)

Chou Hung, John Choi, Steven Gutstein, Matthew Jaswa, Joe Rexwinkle

Proceedings Volume 11746, 117462U (2021) https://doi.org/10.1117/12.2585828

KEYWORDS: Detection and tracking algorithms, Weapons, Target acquisition, Machine vision, Computer vision technology

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Current paradigms for collecting data to train fieldable computer vision (CV) algorithms are inefficient and expensive in terms of time and resources, and they have limited ability to adapt to changing target signatures. By leveraging opportunistic sensing of the operator’s natural behavior (e.g., firing a weapon, placing markers on a map), it is possible to triage the data for CV algorithms. When paired with an After Action Review (AAR) to confirm target presence and location, the resulting pipeline can efficiently update the CV for changing target signatures. Subjects (n = 10) participated in a simulated mission in which they were asked to mark video frames in which targets belonging to three classes first appeared, then perform a brief AAR in which they were asked to mark target locations in these frames. The CV (Detectron2) was initially trained on one target class, then retrained on instances of a novel class acquired by this pipeline. We report that retraining the CV on as few as 8 images resulted in good localization performance (AP₅₀ 85.6, AR 42.5) on unseen test images to this novel class. Ten minutes of retraining (600 iterations) was sufficient for good performance (AP₅₀ 58.3, AR 27.5). Data augmentation via random occlusions and apertures (‘bubbling’) boosted the training set 192-fold and improved the ratio of hits to false alarms and improved resilience to naturalistic occlusion and small sizes (AP 80-100 and AR 80). These results support our approach as an efficient method to adapt CV via partially labeled operational data.

Proceedings Article | 23 April 2020 Paper

Minimizing data requirements for soldier-interactive AI/ML applications through opportunistic sensing

Brent Lance, Gabriella Larkin, Jonathan Touryan, Joe Rexwinkle, Steven Gutstein, Stephen Gordon, Osben Toulson, John Choi, Ali Mahdi, Chou Hung, Vernon Lawhern

Proceedings Volume 11413, 1141306 (2020) https://doi.org/10.1117/12.2564515

KEYWORDS: Weapons, Data modeling, Target recognition, Environmental sensing, Sensors, Augmented reality, Algorithm development

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The application of Artificial Intelligence and Machine Learning (AI/ML) technologies to Aided Target Recognition (AiTR) systems will significantly improve target acquisition and engagement effectiveness. Although, the effectiveness of these AI/ML technologies is based on the quantity and quality of labeled training data, there is very limited labeled operational data available. Creating this data is both time-consuming and expensive, and AI/ML technologies can be brittle and unable to adapt to changing environmental conditions or adversary tactics that are not represented in the training data. As a result, continuous operational data collection and labeling are required to adapt and refine these algorithms, but collecting and labeling operational data carries potentially catastrophic risks if it requires Soldier interaction that degrades critical task performance. Addressing this problem to achieve robust, effective AI/ML for AiTR requires a multi-faceted approach integrating a variety of techniques such as generating synthetic data and using algorithms that learn on sparse and incomplete data. In particular, we argue that it is critical to leverage opportunistic sensing: obtaining operational data required to train and validate AI/ML algorithms from tasks the operator is already doing, without negatively affecting performance on those tasks or requiring any additional tasks to be performed. By leveraging the Soldier’s substantial skills, capabilities, and adaptability, it will be possible to develop effective and adaptive AI/ML technologies for AiTR in the future Multi- Domain Operations (MDO) battlefield.

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