Nec Corporation

Amit Redkar

Srimat T Chakradhar

Biplob Kumar Debnath

Kunal Rao

Yi Yang

Murugan Sankaradas

Utsav Drolia

Ravi Kailasam Rajendran

A method for real-time cross-spectral object association and depth estimation is presented. The method includes synthesizing, by a cross-spectral generative adversarial network (CS-GAN), visual images from different data streams obtained from a plurality of different types of sensors, applying a feature-preserving loss function resulting in real-time pairing of corresponding cross-spectral objects, and applying dual bottleneck residual layers with skip connections to accelerate real-time inference and to accelerate convergence during model training.

Real-time cross-spectral object association and depth estimation

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Princeton, NJ, United States of America

Amit Redkar