The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W18, 79–84, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W18-79-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W18, 79–84, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W18-79-2019

  29 Nov 2019

29 Nov 2019

JOINT GEOMETRIC CALIBRATION OF COLOR AND THERMAL CAMERAS FOR SYNCHRONIZED MULTIMODAL DATASET CREATING

V. A. Knyaz1,2 and P. V. Moshkantsev1 V. A. Knyaz and P. V. Moshkantsev
  • 1State Research Institute of Aviation System (GosNIIAS), 125319, 7, Victorenko str., Moscow, Russia
  • 2Moscow Institute of Physics and Technology (MIPT), Russia

Keywords: calibration, accuracy, thermal imaging, multimodal data, 3D reconstruction

Abstract. With increasing performance and availability of thermal cameras the number of applications using them in various purposes grows noticeable. Nowadays thermal vision is widely used in industrial control and monitoring, thermal mapping of industrial areas, surveillance and robotics which output huge amount of thermal images. This circumstance creates the necessary basis for applying deep learning which demonstrates the state-of-the-art performance for the most complicated computer vision tasks. Using different modalities for scene analysis allows to outperform results of mono-modal processing, but in case of machine learning it requires synchronized annotated multimodal dataset. The prerequisite condition for such dataset creating is geometric calibration of sensors used for image acquisition. So the purpose of the performed study was to develop a technique for joint calibration of color and long wave infra-red cameras which are to be used for collecting multimodal dataset needed for the tasks of computer vision algorithms developing and evaluating.

The paper presents the techniques for camera parameters estimation and experimental evaluation of interior orientation of color and long wave infra-red cameras for further exploiting in datasets collecting. Also the results of geometrically calibrated camera exploiting for 3D reconstruction and 3D model realistic texturing based on visible and thermal imagery are presented. They proved the effectivity of the developed techniques for collecting and augmenting synchronized multimodal imagery dataset for convolutional neural networks model training and evaluating.