Recursive Bayesian Filtering for Multiple Human Pose Tracking from Multiple Cameras

30/11/2020

Recursive Bayesian Filtering for Multiple Human Pose Tracking from Multiple Cameras

Oh-Hun Kwon, Julian Tanke, Jürgen Gall

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Abstract: 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.

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