An Outdoor Active Camera Relocalization Method Based on Skyline Matching

Abstract

The present invention relates to an outdoor active camera relocation method based on skyline matching, which is characterized in that it comprises the following steps: the reference observation image I R According to the semantics, it is divided into two parts, the sky area and the non-sky area, and the boundary line of the two parts is obtained, that is, the skyline of the reference image, and it is discretized into key points, and the skyline point set of the reference image is obtained; the current observation image I is obtained T The skyline point set; match two sets of skyline point sets to obtain the matching relationship between the reference image and the current image; obtain the relative camera pose P between the reference observation and the current observation according to the obtained matching relationship S; according to the camera in the translation direction The step size information of the upper motion judges whether the relocation process is completed; if the relocation is completed, it will terminate; if it is not completed, iterative steps 2 to 5 will be repeated.

Description

technical field [0001] The invention belongs to the fields of artificial intelligence and computer vision, relates to active vision technology, in particular to an outdoor active camera relocation method based on skyline matching. Background technique [0002] Active camera repositioning aims to physically and truly restore the camera's six-degree-of-freedom pose to the same as when the reference image was taken. It plays an important role in environmental monitoring, preventive protection of historical and cultural heritage, small change detection and other fields, and is an important application of active vision technology. The active camera relocalization process includes the relative pose estimation of the camera and the dynamic adjustment of the camera, and the adjustment of the camera is completed by the robot platform. [0003] The current state-of-the-art active camera relocalization algorithms have achieved great success in the task of detecting small changes in cu...

AI Technical Summary

Problems solved by technology

[0004] However, when the lighting conditions (direction and intensity) of previous observations are different, the monitoring results of existing methods are not satisfactory

Significant differences in illumination will change the appearance of the scene (especially the ubiquitous three-dimensional structure scene), and then the feature point descriptors in the image involving pose estimation will change, resulting in the failure of camera pose estimation

In addition, if the background (non-monitoring object area) in the observation scene changes greatly, for example, the vegetation near the monitored ancient building may change drastically in different seasons (even structural changes), which will significantly increase the probability of mis-matching feature points in the image. Quantity, which seriously affects the accuracy of relocation results

The above two situations that are common in actual tasks will seriously damage the accuracy of existing active camera relocation methods, resulting in unreliable actual monitoring results, which cannot support outdoor relocation operations under uncontrollable environmental conditions

Images