no code implementations • 4 Jul 2023 • Andreas Doering, Marius Wiggert, Hanna Krasowski, Manan Doshi, Pierre F. J. Lermusiaux, Claire J. Tomlin
We demonstrate the safety of our approach in such realistic situations empirically with large-scale simulations of a vessel navigating in high-risk regions in the Northeast Pacific.
no code implementations • 9 Jun 2023 • Andreas Doering, Juergen Gall
Multi-person pose tracking is an important element for many applications and requires to estimate the human poses of all persons in a video and to track them over time.
1 code implementation • 16 Nov 2021 • Di Chen, Andreas Doering, Shanshan Zhang, Jian Yang, Juergen Gall, Bernt Schiele
Video-based person re-identification (re-ID) is an important technique in visual surveillance systems which aims to match video snippets of people captured by different cameras.
Representation Learning Video-Based Person Re-Identification
no code implementations • 12 Nov 2020 • Andreas Doering, Di Chen, Shanshan Zhang, Bernt Schiele, Juergen Gall
For that reason, we present PoseTrackReID, a large-scale dataset for multi-person pose tracking and video-based person re-ID.
no code implementations • ECCV 2020 • Umer Rafi, Andreas Doering, Bastian Leibe, Juergen Gall
Instead of training the network for estimating keypoint correspondences on video data, it is trained on a large scale image datasets for human pose estimation using self-supervision.
Multi-Person Pose Estimation Multi-Person Pose Estimation and Tracking +1
no code implementations • 26 Apr 2019 • Rania Briq, Andreas Doering, Juergen Gall
We propose a joint model of human joint detection and association for 2D multi-person pose estimation (MPPE).
no code implementations • 11 May 2018 • Andreas Doering, Umar Iqbal, Juergen Gall
The general formulation of our temporal network allows to rely on any multi person pose estimation approach as spatial network.
no code implementations • 8 May 2017 • Umar Iqbal, Andreas Doering, Hashim Yasin, Björn Krüger, Andreas Weber, Juergen Gall
To this end, we first convert the motion capture data into a normalized 2D pose space, and separately learn a 2D pose estimation model from the image data.
Ranked #37 on Monocular 3D Human Pose Estimation on Human3.6M