People

Friedhelm Hamann

Doctoral Researcher

Computer Science

TU Berlin

 

Email:

 

Photo: SCIoI

← People Overview

Friedhelm Hamann

Friedhelm Hamann

Photo: SCIoI

Friedhelm Hamann is a doctoral researcher with SCIoI and a member of the Robotic Interactive Perception group. He is interested in advancing algorithms for visual sensor data to extract high-level information and improve the perception system of artificial agents. His research (Project 36) focuses on the use of event cameras, which are inspired by the human visual system. At SCIoI, he aims to integrate computer vision algorithms into robotic systems to develop tools for the behavioral analysis of animals. Friedhelm received his B.Sc. in Electrical Engineering from University of Rostock in 2019 and completed his M.Sc. in Electrical Engineering and Information Technology at RWTH Aachen University in 2021.

Projects

Friedhelm Hamann is member of:

• Project 36: Active tracking using bioinspired event-based vision – Towards improving interpretability of individual and collective behavior
Hamann, F., Gehrig, D., Febryanto, F., Daniilidis, K., & Gallego, G. (2025). ETAP: Event-based Tracking of Any Point. IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 17. https://doi.org/10.48550/arXiv.2412.00133
Guo, S., Hamann, F., & Gallego, G. (2025). Unsupervised Joint Learning of Optical Flow and Intensity with Event Cameras. arXiv. https://doi.org/10.48550/arXiv.2503.17262
Wang, Z., Hamann, F., Chaney, K., Jiang, W., Gallego, G., & Daniilidis, K. (2025). Event-based Continuous Color Video Decompression from Single Frames. IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR) Workshops. https://doi.org/10.48550/arXiv.2312.00113
Hamann, F., Ghosh, S., Martínez, I. J., Hart, T., Kacelnik, A., & Gallego, G. (2024). Low-power, Continuous Remote Behavioral Localization with Event Cameras. IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 18612–18621. https://doi.org/10.1109/CVPR52733.2024.01761
Hamann, F., Li, H., Mieske, P., Lewejohann, L., & Gallego, G. (2024). MouseSIS: A Frames-and-Events Dataset for Space-Time Instance Segmentation of Mice. European Conference on Computer Vision (ECCV) Workshops, 156–173. https://doi.org/10.1007/978-3-031-92460-6_10
Hamann, F., Ghosh, S., Martínez, I. J., Hart, T., Kacelnik, A., & Gallego, G. (2024). Fourier-based Action Recognition for Wildlife Behavior Quantification with Event Cameras. Advanced Intelligent Systems, 2400353. https://doi.org/10.1002/aisy.202400353
Hamann, F., Wang, Z., Asmanis, I., Chaney, K., Gallego, G., & Daniilidis, K. (2024). Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation. European Conference on Computer Vision (ECCV), 18–37. https://doi.org/10.1007/978-3-031-72646-0_2
Shiba, S., Hamann, F., Aoki, Y., & Gallego, G. (2023). Event-based Background-Oriented Schlieren. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–16. https://doi.org/10.1109/TPAMI.2023.3328188
Hamann, F., & Gallego, G. (2022). Stereo Co-capture System for Recording and Tracking Fish with Frame- and Event Cameras. International Conference on Pattern Recognition (ICPR), Workshop on Visual observation and analysis of Vertebrate And Insect Behavior. https://arxiv.org/abs/2207.07332

NSF AccelNet NeuroPac Fellowship

Research

An overview of our scientific work

See our Research Projects