Towards Global People Detection and Tracking using Multiple Depth Sensors
Johannes Wetzel, Samuel Zeitvogel, Astrid Laubenheimer, Michael Heizmann · 2018
In this work a novel approach for multi depth sensor person detection and tracking from top view is presented. We propose a probabilistic framework formulating the problem of people detection in multiple overlapping depth images as an inverse problem. As a generative forward model, we employ a simple differentiable 3D person model allowing us to detect people from arbitrary viewpoints. Furthermore, we extend our probabilistic framework to allow for tracking of individuals over time. Finally, we show how to solve for the global person trajectories exploiting differentiable rendering. The preliminary evaluation shows promising qualitative results of our approach on samples of three stereo vision based depth sensors observing an indoor scene.