PoseFusion2: Simultaneous Background Reconstruction and Human Shape Recovery in Real-time

Huayan Zhang, Tianwei Zhang, Tin Lun Lam, Sethu Vijayakumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Dynamic environments that include unstructured moving objects pose a hard problem for Simultaneous Localization and Mapping (SLAM) performance. The motion of rigid objects can be typically tracked by exploiting their texture and geometric features. However, humans moving in the scene are often one of the most important, interactive targets – they are very hard to track and reconstruct robustly due to non-rigid shapes. In this work, we present a fast, learning-based human object detector to isolate the dynamic human objects and realise a real-time dense background reconstruction framework. We go further by estimating and reconstructing the human pose and shape. The final output environment maps not only provide the dense static backgrounds but also contain the dynamic human meshes and their trajectories. Our Dynamic SLAM system runs at around 26 frames per second (fps) on GPUs, while additionally turning on accurate human pose estimation can be executed at up to 10fps.
Original languageEnglish
Title of host publication2021 IEEE/RSJ International Confence on Intelligent Robots and Systems
Number of pages8
Publication statusAccepted/In press - 30 Jun 2021
Event2021 IEEE/RSJ International Conference on Intelligent Robots and Systems - Online, Prague, Czech Republic
Duration: 27 Sep 20211 Oct 2021
https://www.iros2021.org/

Conference

Conference2021 IEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS 2021
Country/TerritoryCzech Republic
CityPrague
Period27/09/211/10/21
Internet address

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