- Do We Still Need Non-Maximum Suppression? Accurate Confidence Estimates and Implicit Duplication Modeling with IoU-Aware Calibration. , 2023 mehr…
- EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye View. , 2023 mehr…
- The Box Size Confidence Bias Harms Your Object Detector. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 mehr…
- Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual Cities. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2023 mehr…
- Explainable Model-Agnostic Similarity and Confidence in Face Verification. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2023 mehr…
- Face Morphing: Fooling a Face Recognition System Is Simple! , 2022 mehr…
- Towards a Deeper Understanding of Skeleton-Based Gait Recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022 mehr…
- Gaitgraph: Graph Convolutional Network for Skeleton-Based Gait Recognition. 2021 IEEE International Conference on Image Processing (ICIP), IEEE, 2021, 2314-2318 mehr…
- Lightweight Multi-Branch Network For Person Re-Identification. 2021 IEEE International Conference on Image Processing (ICIP), IEEE, 2021, 1129-1133 mehr…
- Attention-Based Partial Face Recognition. 2021 IEEE International Conference on Image Processing (ICIP), IEEE, 2021, 2978-2982 mehr…
Torben Teepe, M.Sc.
Technische Universität München
Lehrstuhl für Mensch - Maschine - Kommunikation (Prof. Hemmert komm.)
Postadresse
Postal:
Arcisstr. 21
80333 München
- Tel.: +49 (89) 289 - 28547, 28554
- Raum: 0101.EG.144
- t.teepe@tum.de
Forschungsgebiete
• Deep Learning
• Computer Vision
• Gait Recognition
Publikationen
Projekte
Grundrechtskonforme Gesichtserkennung im öffentlichen Raum
Projektpartner: Uniscon GmbH, Tüv Süd Digital Services, AXIS GmbH
Projektzeitraum: 01.10.2018 - 30.09.2020
Lehre
• Signaldarstellung für MSE (WS 2019)
Studentische Arbeiten
Bei Anfragen zu studentischen Arbeiten reichen Sie bitte folgende Unterlagen mit ein:
• Aktueller Lebenslauf
• Notenauszug
• Bisherige Erfahrungen aus dem Themengebiet
• Starttermin
Offen
Alle ausgeschriebenen Arbeiten finden Sie hier.
Finished
2021
• Skeleton-Based Gait Recognition with Graph Convolutional Network (Forschungspraxis)
2020
• Skeleton-based Gait Recognition using Spatial-Temporal Graph Convolutional Networks (Master's Thesis)
2019
• MOTS: Multi-Object Tracking and Segmentation (Scientific Seminar)
• Attention-Aware Compositional Network for Person Re-identification (Scientifc Seminar)