Ms. Rohita Mocharla Profile

Rohita Mocharla

at Johns Hopkins Univ Applied Physics Lab

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Proceedings Article | 14 June 2023 Poster + Paper

Leveraging synthetic data for robust gesture recognition

Kapil Katyal, Rama Chellappa, Ketul Shah, Arun Reddy, Judy Hoffman, William Paul, Rohita Mocharla, David Handelman, Celso de Melo

Proceedings Volume 12529, 125290Y (2023) https://doi.org/10.1117/12.2663827

KEYWORDS: Gesture recognition, Robots, Detection and tracking algorithms, Education and training, Image classification, Semantics, Action recognition, Image segmentation, RGB color model, Video

Read Abstract +

Effective communication and control of a team of humans and robots is critical for a number DoD operations and scenarios. In an ideal case, humans would communicate with the robot teammates using nonverbal cues (i.e., gestures) that work reliably in a variety of austere environments and from different vantage points. A major challenge is that traditional gesture recognition algorithms using deep learning methods require large amounts of data to achieve robust performance across a variety of conditions. Our approach focuses on reducing the need for “hard-to-acquire” real data by using synthetically generated gestures in combination with synthetic-to-real domain adaptation techniques. We also apply the algorithms to improve the robustness and accuracy of gesture recognition from shifts in viewpoints (i.e., air to ground). Our approach leverages the soon-to-be released dataset called Robot Control Gestures (RoCoG-v2), consisting of corresponding real and synthetic videos from ground and aerial viewpoints. We first demonstrate real-time performance of the algorithm running on low-SWAP, edge hardware. Next, we demonstrate the ability to accurately classify gestures from different viewpoints with varying backgrounds representative of DoD environments. Finally, we show the ability to use the inferred gestures to control a team of Boston Dynamic Spot robots. This is accomplished using inferred gestures to control the formation of the robot team as well as to coordinate the robot’s behavior. Our expectation is that the domain adaptation techniques will significantly reduce the need for real-world data and improve gesture recognition robustness and accuracy using synthetic data.

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