Information fusion method of kinect depth camera and thermal infrared camera

Abstract

The invention discloses an information fusion method of a kinect depth camera and a thermal infrared camera. The information fusion method comprises the steps of selecting a calibration plate; acquiring images of different angles on the calibration plate through a near-infrared short-wave camera and an RGB camera of the kinect depth camera; carrying out heating treatment on the calibration plate,and acquiring the images of different angles of the calibration plate through the thermal infrared camera and the RGB camera of the kinect depth camera; acquiring the external parameters of the near-infrared short-wave camera, the RGB camera, and the thermal infrared camera by means of a small-hole imaging model and a double-target calibration principle; acquiring the geometric relation between the near-infrared short-wave camera and the RGB camera; acquiring the geometric relation between the thermal infrared camera and the RGB camera; and according to the geometric relation between the near-infrared short-wave camera and the RGB camera and the geometric relation between the thermal infrared camera and the RGB camera, performing the information fusion on the thermal infrared camera, the near-infrared short-wave camera and the RGB camera.

Description

technical field [0001] The invention relates to the field of structured light multi-sensor information fusion, in particular to an information fusion method of a Kinect depth camera and a thermal infrared camera. Background technique [0002] Compared with traditional depth information acquisition methods, Kinect has the characteristics of portability and low price. Kinect can be used to directly obtain the depth information of indoor scenes and target areas. The combination of RGB information can reconstruct the real 3D information of the scene and the target area. However, based on the limitations of the Kinect imaging principle, Kinect has poor image quality in rainy days, foggy days, and low light conditions; in addition, Kinect’s depth camera and RGB camera have limited calibration parameters at the factory, resulting in unsatisfactory reconstruction results. In traditional machine learning and deep learning, only relying on texture information and three-dimensional in...

AI Technical Summary

Problems solved by technology

However, based on the limitations of the Kinect imaging principle, Kinect has poor image quality in rainy days, foggy days, and low light conditions; in addition, Kinect’s depth camera and RGB camera have limited calibration parameters at the factory, resulting in unsatisfactory reconstruction results. In traditional machine learning and deep learning, only relying on texture information and three-dimensional information is difficult to meet the requirements of input information. Therefore, it is necessary to provide more reliable conditions as input information in the process of machine learning and deep learning to improve the recognition rate of the system.

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