Real-time space target feature point tracking method suitable for space tethered robot

Abstract

The invention relates to a real-time space target feature point tracking method suitable for a space tethered robot. The method is mainly formed by a feature point extraction algorithm, a feature point matching algorithm, discrete point elimination, accurate positioning and a self-adaptive strategy. Feature points extracted through an improved SURF algorithm are strong in robustness and stability. The method enables the feature point matching accuracy and target positioning accuracy to be high, and is suitable for stable tracking of targets which have rapid changes in positions and gestures and are simple in structure in a multi-scale image sequence. The method has robustness in various types challenging situations, and the tracking process has short-term anti-occlusion capacity. The method can be used for achieving the real-time detection and tracking of a plurality of types of targets, is suitable for real-time robustness tracking for the space targets which have rapid changes in positions and gestures and are simple in structure in the multi-scale image sequence.

Description

technical field [0001] The invention belongs to the field of pattern recognition, relates to technologies such as image processing and computer vision, and relates to a real-time space object feature point tracking method suitable for a space tether robot. Background technique [0002] Due to the characteristics of low cost, passive detection, and strong robustness, visual sensors have received more and more attention in the tracking of moving targets. For example, space tethered robots use binocular vision to track space targets, missiles and other aircraft use vision Sensors track aerial targets, etc. [0003] However, the tracking of spatial objects in video sequences is a very complex task, and there are many challenges, such as the sequence contains object rotation, scale change, motion blur, and other related objects. Moreover, spatial objects usually have the following characteristics: simple structure and texture features, single grayscale features, large scale chan...

AI Technical Summary

Problems solved by technology

[0008] For the tracking problem of space targets, the KLT algorithm mentioned above has the problem of low detection accuracy of feature points, inaccurate tracking caused by poor matching accuracy when the target moves fast, and the detection time of SIFT algorithm in Liu Yu and Wang Jingdong's method is long and lacks feature point self-adaptation update strategy

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