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Research
My research is on machine learning and computer vision for video processing. Here are my recent
projects and papers:
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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: Fooling a Face Recognition System Is Simple!
S. Hörmann,
T. Kong,
T. Teepe,
F. Herzog,
M. Knoche,
and G. Rigoll
arXiv
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
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code
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video
/
publication
A comparative analysis of different spatiotemporal modeling techniques for action and
gesture
recognition.
Teaching
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)
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