  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}.
