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 Scientific Computing in Göttingen with a focus on mathematical image processing.

Email / Google Scholar / Github / Twitter / LinkedIn

Research

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 arXiv, 2022 arXiv / 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 arXiv, 2022 arXiv / code *Accepted for the 17th IEEE Computer Society Workshop on Biometrics 2022* 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.
	The Box Size Confidence Bias Harms Your Object Detector J. Gilg, T. Teepe, F. Herzog, and G. Rigoll arXiv, 2022 arXiv / code *Accepted for WACV 2023* 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 *Accepted for ICIP 2021* 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 *Accepted for ICIP 2021* 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 *Accepted for ICIP 2021* 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 arXiv, 2020 arXiv / code *Accepted for Computer Vision and Image Understanding Journal* 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 *Accepted for ICPR International Workshops 2021* A comparative analysis of different spatiotemporal modeling techniques for action and gesture recognition.
	sparselandtools: A Python package for sparse representations and dictionary learning, including matching pursuit, K-SVD and applications Python package via Zenodo (10.5281/zenodo.4916395), 2021 Sparselandtools is a Python 3 package that provides implementations for sparse representations and dictionary learning.

Teaching

Summer 2022

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)

Supervision

I'm supervising theses in the areas of computer vision and pattern recognition. Due to the high number of applications, I can only respond to complete applications with current CV and transcript of records.

Professional Service

Reviewer for the NVIDIA AI City Challenge 2022 CVPR Workshop
Reviewer for the IEEE International Conference on Image Processing 2022
Reviewer for the NVIDIA AI City Challenge 2021 CVPR Workshop

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