Visual-Thermal Camera Dataset Release and Multi-Modal Alignment without Calibration Information

TL;DR

This paper introduces a new dataset of visual and thermal camera data with aligned frames achieved without calibration, enabling better multi-modal feature analysis using mutual information-based registration.

Contribution

The paper presents a novel method for aligning visual and thermal images without calibration, and releases a dataset with aligned frames and calibration parameters for research use.

Findings

Successful alignment of visual and thermal images without calibration

Use of mutual information metric for multi-modal image registration

Dataset includes raw and aligned frames with calibration data

Abstract

This report accompanies a dataset release on visual and thermal camera data and details a procedure followed to align such multi-modal camera frames in order to provide pixel-level correspondence between the two without using intrinsic or extrinsic calibration information. To achieve this goal we benefit from progress in the domain of multi-modal image alignment and specifically employ the Mattes Mutual Information Metric to guide the registration process. In the released dataset we release both the raw visual and thermal camera data, as well as the aligned frames, alongside calibration parameters with the goal to better facilitate the investigation on common local/global features across such multi-modal image streams.