Using Panoramic Videos for Multi-person Localization and Tracking in a 3D Panoramic Coordinate

TL;DR

This paper introduces a low-cost, efficient method for 3D multi-person localization and tracking using panoramic videos from four normal cameras, transforming 2D images into 3D coordinates and associating appearance and trajectory data.

Contribution

The work presents a novel approach that leverages standard cameras and geometric transformations for 3D multi-person localization and tracking, reducing reliance on expensive LiDAR systems.

Findings

Effective on three datasets, including a new one created by the authors.

Achieves accurate 3D multi-person localization and tracking from panoramic videos.

Demonstrates computational efficiency and low cost compared to LiDAR-based methods.

Abstract

3D panoramic multi-person localization and tracking are prominent in many applications, however, conventional methods using LiDAR equipment could be economically expensive and also computationally inefficient due to the processing of point cloud data. In this work, we propose an effective and efficient approach at a low cost. First, we obtain panoramic videos with four normal cameras. Then, we transform human locations from a 2D panoramic image coordinate to a 3D panoramic camera coordinate using camera geometry and human bio-metric property (i.e., height). Finally, we generate 3D tracklets by associating human appearance and 3D trajectory. We verify the effectiveness of our method on three datasets including a new one built by us, in terms of 3D single-view multi-person localization, 3D single-view multi-person tracking, and 3D panoramic multi-person localization and tracking. Our code and dataset are available at \url{https://github.com/fandulu/MPLT}.