Fabian Herzog

I am a research assistant and PhD student at the Institute of Human-Machine Communication of the Technical University of Munich, Germany. My main research is in computer vision and pattern recognition.

Prior to joining TU Munich, I did my master's in computer science in Göttingen with a focus on scientific computing.

Email  /  Google Scholar  /  Github  /  LinkedIn

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My research is on machine learning and computer vision for video processing. Here are my recent projects and papers:

Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual Cities
F. Herzog, J. Chen, T. Teepe, J. Gilg, S. Hörmann, and G. Rigoll
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2023
Open Access / code / project website

We propose a massive new dataset for multi-camera vehicle tracking in non-overlapping and overlapping camera views with full 2D and 3D annotations and instance, semantic, and panoptic segmentation ground truth.

Check out our project website to download the new dataset!

Towards a Deeper Understanding of Skeleton-based Gait Recognition
T. Teepe, J. Gilg, F. Herzog, S. Hörmann, and G. Rigoll
IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022
arXiv / code

We propose an approach based on Graph Convolutional Networks (GCNs) that combines higher-order inputs, and residual networks to an efficient architecture for gait recognition.

Face Morphing
Face Morphing: Fooling a Face Recognition System Is Simple!
S. Hörmann, T. Kong, T. Teepe, F. Herzog, M. Knoche, and G. Rigoll

A new approach using a GAN for face morphing shows that even recent face recognition systems struggle to distinguish morphed faces from both identities, making them vulnerable to face morphing attacks.

The Box Size Confidence Bias Harms Your Object Detector
J. Gilg, T. Teepe, F. Herzog, and G. Rigoll
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , 2023
arXiv / code

We demonstrate how to modify the histogram binning calibration to improve performance through conditional confidence calibration.

Face Aggregation Network For Video Face Recognition
S. Hörmann, Z. Chao, M. Knoche, F. Herzog, and G. Rigoll
IEEE International Conference on Image Processing (ICIP), 2021
IEEE Article

We suggest a permutation invariant U-Net architecture capable of processing an arbitrary number of frames in video face recognition.

GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition
T. Teepe, A. Khan, J. Gilg, F. Herzog, S. Hörmann, and G. Rigoll
IEEE International Conference on Image Processing (ICIP), 2021
arXiv / code

A novel Graph Convolutional Network (GCN) to obtain a modern model-based approach for gait recognition.

Lightweight Multi-Branch Network for Person Re-Identification
F. Herzog, X. Ji, T. Teepe, S. Hörmann, J. Gilg, and G. Rigoll
IEEE International Conference on Image Processing (ICIP), 2021
arXiv / code

Lightweight network that combines global, part-based, and channel features in a unified multi-branch architecture.

Dissected 3D CNNs: Temporal Skip Connections for Efficient Online Video Processing
O. Köpüklü, S. Hörmann, F. Herzog, H. Cevikalp, and G. Rigoll
Journal of Computer Vision and Image Understanding, 2022
arXiv / code

We propose dissected 3D CNNs to address several serious handicaps of 3D ResNet models during online operation.

Comparative Analysis of CNN-Based Spatiotemporal Reasoning in Videos
O. Köpüklü, F. Herzog, and G. Rigoll
ICPR International Workshops and Challenges. ICPR 2021.
arXiv / code / video / publication

A comparative analysis of different spatiotemporal modeling techniques for action and gesture recognition.


Summer 2023

Summer 2022

  • Mensch-Maschine-Kommunikation II (Übungen)
  • Signal- und Musterverarbeitung (Übungen)
  • Musikalische Akustik (Assistenz)

Summer 2021

  • Mensch-Maschine-Kommunikation II (Übungen)

Winter 2020 / 2021

  • Praktikum Bild- und Tonverarbeitung

Summer 2020

  • Mensch-Maschine-Kommunikation II (Übungen)

Winter 2019 / 2020

  • Praktikum Bild- und Tonverarbeitung
  • Praktikum System- und Schaltungstechnik

Summer 2019

  • Mensch-Maschine-Kommunikation II (Übungen)
Professional Service
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