30 April 2018 Occluded object reconstruction for first responders with augmented reality glasses using conditional generative adversarial networks
Author Affiliations +
Abstract
Firefighters suffer a variety of life-threatening risks, including line-of-duty deaths, injuries, and exposures to hazardous substances. Support for reducing these risks is important. We built a partially occluded object reconstruction method on augmented reality glasses for first responders. We used a deep learning based on conditional generative adversarial networks to train associations between the various images of flammable and hazardous objects and their partially occluded counterparts. Our system then reconstructed an image of a new flammable object. Finally, the reconstructed image was superimposed on the input image to provide "transparency". The system imitates human learning about the laws of physics through experience by learning the shape of flammable objects and the flame characteristics.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kyongsik Yun, Kyongsik Yun, Thomas Lu, Thomas Lu, Edward Chow, Edward Chow, } "Occluded object reconstruction for first responders with augmented reality glasses using conditional generative adversarial networks ", Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 106490T (30 April 2018); doi: 10.1117/12.2305151; https://doi.org/10.1117/12.2305151
PROCEEDINGS
7 PAGES + PRESENTATION

SHARE
Back to Top