Occluded object reconstruction for first responders with augmented reality glasses using conditional generative adversarial networks

Kyongsik Yun; Thomas Lu; Edward Chow

doi:10.1117/12.2305151

30 April 2018 Occluded object reconstruction for first responders with augmented reality glasses using conditional generative adversarial networks

Kyongsik Yun, Thomas Lu, Edward Chow

Author Affiliations +

Proceedings Volume 10649, Pattern Recognition and Tracking XXIX; 106490T (2018) https://doi.org/10.1117/12.2305151
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

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.

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Kyongsik Yun, Thomas Lu, and 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); https://doi.org/10.1117/12.2305151

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