Recursive Bayesian Filtering for Multiple Human Pose Tracking from Multiple Cameras

Oh-Hun Kwon, Julian Tanke, Juergen Gall; Proceedings of the Asian Conference on Computer Vision (ACCV), 2020

Markerless motion capture allows the extraction of multiple 3D human poses from natural scenes, without the need for a controlled but artificial studio environment or expensive hardware. In this work we present a novel tracking algorithm which utilizes recent advancements in 2D human pose estimation as well as 3D human motion anticipation. During the prediction step we utilize an RNN to forecast a set of plausible future poses while we utilize a 2D multiple human pose estimation model during the update step to incorporate observations. Casting the problem of estimating multiple persons from multiple cameras as a tracking problem rather than an association problem results in a linear relationship between runtime and the number of tracked persons. Furthermore, tracking enables our method to overcome temporary occlusions by relying on the prediction model. Our approach achieves state-of-the-art results on popular benchmarks for 3D human pose estimation and tracking.