Multi-Person Tracking Method Robust to Dynamic Viewport Changes for AR apps

Abstract

Augmented reality (AR) devices have gained a lot of attention in recent years due to their ability to enhance people’s abilities through 2D/3D spatial sensing and recognition functions. RGB cameras are most often used as sensors in this type of spatial recognition, and a particularly important task is the detection and tracking of objects and people in physical space. However, the camera positions and orientations on AR devices such as smartphones and smart glasses, frequently change due to the user wearing them on their head, leading to non-linear and complex motion in the video frames and reducing the accuracy of tracking people. To address this issue, the proposed method combines person re-identification based on deep metric learning with trajectory prediction to estimate the person’s sequential positions in 3D space around the camera. The experimental result shows 95.45% accuracy with our dataset.