Real-time cross-spectral object association and depth estimation

US12050664B2 · kind B2 · utility

Assignee

• NEC CORPORATION · JP

Inventors

• Murugan Sankaradas
• Kunal Rao · Monroe, US
• Yi Yang · Princeton, US
• Biplob Debnath · Minneapolis, US
• Utsav Drolia · Milpitas, US
• Srimat Chakradhar · Manalapan, US
• Amit Redkar · Princeton, US
• Ravi Kailasam Rajendran · Princeton, US

Key dates

• Filing date: Oct 6, 2021
• Grant date: Jul 30, 2024
• Expiry date: Feb 3, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G06T2210/12
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

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.