Dual Band Video Thermography Near Ambient Conditions

2025-09-14

AI Summary

At near-ambient temperatures, long-wave infrared (LWIR) radiation comprises comparable and dynamically coupled contributions from surface self-emission and environmental reflection, rendering accurate estimation of emissivity, true temperature, and reflectivity challenging. To address this, we propose the first dual-band thermal imaging framework for dynamic scenes—departing from conventional assumptions of single-dominant-radiation components or static backgrounds—and enabling time-varying separation of emission and reflection. Our method establishes a dual-band radiative transfer model, integrates multispectral image analysis with background-decoupling algorithms, and leverages synchronized dual-thermal-camera acquisition to jointly estimate spatially varying surface emissivity, true temperature, and reflectance. Extensive experiments across diverse materials and complex real-world scenarios—including a heated liquid-filled glass and a moving human subject—demonstrate high accuracy and robustness. This work establishes a new paradigm for physics-informed thermal vision understanding.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Long-wave infrared radiation captured by a thermal camera consists of two components: (a) light from the environment reflected or transmitted by a surface, and (b) light emitted by the surface after undergoing heat transport through the object and exchanging heat with the surrounding environment. Separating these components is essential for understanding object properties such as emissivity, temperature, reflectance and shape. Previous thermography studies often assume that only one component is dominant (e.g., in welding) or that the second component is constant and can be subtracted. However, in near-ambient conditions, which are most relevant to computer vision applications, both components are typically comparable in magnitude and vary over time. We introduce the first method that separates reflected and emitted components of light in videos captured by two thermal cameras with different spectral sensitivities. We derive a dual-band thermal image formation model and develop algorithms to estimate the surface's emissivity and its time-varying temperature while isolating a dynamic background. We quantitatively evaluate our approach using carefully calibrated emissivities for a range of materials and show qualitative results on complex everyday scenes, such as a glass filled with hot liquid and people moving in the background.

Problem

• Separating reflected and emitted light in thermal videos
• Estimating surface emissivity and time-varying temperature
• Isolating dynamic background in near-ambient conditions

Innovation

• Dual-band thermal cameras for separation
• Emissivity and temperature estimation algorithm
• Dynamic background isolation in thermography