Dr. Jason Zutty Profile

Dr. Jason Zutty

Senior Research Engineer at Georgia Tech Research Institute

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Publications (4)

Proceedings Article | 7 June 2024 Presentation + Paper

Range estimation using machine-learned algorithms for passive sensors

Clint Morris, Jason Zutty

Proceedings Volume 13035, 130350Q (2024) https://doi.org/10.1117/12.3015079

KEYWORDS: Motion models, Cameras, Transformers, Machine learning, Autonomous driving, Depth maps, Range image registration

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In this research, we present a depth prediction model designed for a range of applications, moving beyond the traditional scope of assisted and autonomous driving systems. Our model emphasizes absolute accuracy over relative accuracy, tackling the challenge of performance deterioration at extended ranges.

To bolster our novel design, we employed the AirSim Unreal Engine simulator to develop a tailored dataset, capturing various scene locations. This approach aids in mitigating model overfitting to nuances such as textures and colors. With over 2.7 million images from diverse scene locations under different environmental conditions, our dataset provided a rich variety of perspectives and distances for training. We further enriched the dataset with images from 14 RGB and depth sensor pairs, strategically placed at varied pitch and yaw angles on a drone, enhancing the model’s adaptability. Notably, our reliance on simulation data aligns our model closely with real-world scenarios.

At the core of our model are features like the overlap patch embedding block, an optimized self-attention mechanism, and a Mixed-Feed Forward Network. Together, they facilitate improved depth prediction, even at considerable distances. Empirical evaluations show consistent performance across a broad depth range, with a Mean Absolute Percent Error (MAPE) of 5-10% maintained up to 1900 meters. However, performance decreases beyond this range, signaling opportunities for future enhancements.

Regarding real-world results, due to the lack of available supervision, real data was analyzed qualitatively. Preliminary observations suggest that the outcomes appear reasonable and align well with expectations, although quantitative validations remain a direction for future research.

Our research provides statistical evidence and visual illustrations of our model’s capabilities in depth prediction. The combination of our approach and insights from the simulation data suggests potential for further advancements in the field of depth prediction.

Proceedings Article | 12 April 2021 Presentation + Paper

Simulation and optimization-based method of environmental parameter distribution from airborne bathymetric LIDAR data

Thomas Lang, Robert Guild, Christopher Valenta, Domenic Carr, Jason Zutty

Proceedings Volume 11744, 1174406 (2021) https://doi.org/10.1117/12.2585710

KEYWORDS: LIDAR, Laser scattering, Data modeling, Water, Statistical analysis, Signal attenuation, Scattering, Reflectivity, Pulsed laser operation, Optimization (mathematics)

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Proceedings Article | 10 May 2019 Paper

Reducing bathymetric-lidar algorithm uncertainty with genetic programming and the evolutionary multi-objective algorithm design engine

Jason Zutty, Domenic Carr, Rodd Talebi, James Rick, Christopher Valenta, Gregory Rohling

Proceedings Volume 11006, 110061U (2019) https://doi.org/10.1117/12.2519320

KEYWORDS: LIDAR, Data modeling, Computer programming, Machine learning, Algorithm development, Optimization (mathematics), Evolutionary algorithms, Computer simulations, Genetic algorithms

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Proceedings Article | 22 May 2015 Paper

STAC: a comprehensive sensor fusion model for scene characterization

Zsolt Kira, Alan Wagner, Chris Kennedy, Jason Zutty, Grady Tuell

Proceedings Volume 9498, 949804 (2015) https://doi.org/10.1117/12.2178494

KEYWORDS: Cognitive modeling, Sensors, Data modeling, Data fusion, Cognition, Sensor fusion, Algorithm development, Visualization, Detection and tracking algorithms, Cameras

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