High-speed real-time augmented reality tracking algorithm model of camera based on mixed feature points

Abstract

At this stage of augmented reality, simple feature descriptions are mainly used in camera real-time motion tracking, but this is prone to the problem of unstable camera motion tracking. Aiming at the balance between real-time performance and stability, a new method model of real-time camera motion tracking based on mixed features was proposed. By comprehensively using feature points and feature lines as scene features, feature extraction, optimization, and fusion are used to construct hybrid features, and the hybrid features are unified for real-time camera parameter estimation. An image feature optimization method based on scene structure analysis is proposed to meet the computing constraints of mobile terminals. An iterative feature line-screening method is proposed to calculate a stable feature line set, and based on the scene feature composition and feature geometry, a hybrid feature is adaptively constructed to improve the tracking stability of the camera. Based on improved SIFT feature matching target detection and tracking algorithm, a hybrid feature point detection operator detection algorithm is used to achieve rapid feature point extraction, and the speed of descriptor generation is reduced by reducing the feature descriptor vector dimension. The experimental results prove that the proposed target detection and tracking algorithm has good real-time and robustness, and improves the success rate of target detection and tracking.