Human motion tracking and positioning for augmented reality

AR (Augmented reality) is a research hotspot in the current computer application field. AR technology enhances people's understanding and experience of the real environment by adding virtual objects to real scenes to integrate virtual objects with the real environment. Aiming at the weak processing power of intelligent terminals and the characteristics of limited hardware resources, this paper proposes a more effective human motion feature extraction and descriptor algorithm. The feature point detection and positioning method suitable for intelligent terminals is proposed in a targeted manner, which solves the problem of mismatching of similar structures. In addition, this paper proposes an AR-oriented recursive tracking algorithm for human motion. The positional relationship of the current frame is calculated from the position of the previous frame. A combination of ORB (Oriented fast and Rotated Brief) feature descriptors and KLT (Kanade-Lucas-Tomasi) algorithm is adopted. The ORB feature descriptor matched by the first frame image and the reference image is tracked by the KLT tracking algorithm, and the feature descriptor of the previous frame is tracked in the current frame, thereby eliminating the phenomenon of virtual object jitter. The experimental results show that the recursive tracking scheme has better performance in time and precision than the detection tracking scheme.

Automated summary

This paper introduces AR technology and human motion feature extraction and descriptor algorithms tailored for intelligent terminals, including feature point detection and positioning methods and recursive tracking algorithms. The recursive tracking scheme outperforms the detection tracking scheme in terms of time and precision.