1.1k citations · 1.9k across the 24 of their papers we have counts for
36 papers
Pedestrian Environment Model for Automated Driving
Adrian Holzbock, Alexander Tsaregorodtsev, Vasileios Belagiannis
Besides interacting correctly with other vehicles, automated vehicles should also be able to react in a safe manner to vulnerable road users like pedestrians or cyclists. For a saf…
Out-of-Distribution Detection for Monocular Depth Estimation
Julia Hornauer, Adrian Holzbock, Vasileios Belagiannis
In monocular depth estimation, uncertainty estimation approaches mainly target the data uncertainty introduced by image noise. In contrast to prior work, we address the uncertainty…
Automated Automotive Radar Calibration With Intelligent Vehicles
Alexander Tsaregorodtsev, Michael Buchholz, Vasileios Belagiannis
While automotive radar sensors are widely adopted and have been used for automatic cruise control and collision avoidance tasks, their application outside of vehicles is still limi…
Automated Static Camera Calibration with Intelligent Vehicles
Alexander Tsaregorodtsev, Adrian Holzbock, Jan Strohbeck +2
Connected and cooperative driving requires precise calibration of the roadside infrastructure for having a reliable perception system. To solve this requirement in an automated man…
Gesture Recognition with Keypoint and Radar Stream Fusion for Automated Vehicles
Adrian Holzbock, Nicolai Kern, Christian Waldschmidt +2
We present a joint camera and radar approach to enable autonomous vehicles to understand and react to human gestures in everyday traffic. Initially, we process the radar data with…
Extrinsic Camera Calibration with Semantic Segmentation
Alexander Tsaregorodtsev, Johannes Müller, Jan Strohbeck +3
Monocular camera sensors are vital to intelligent vehicle operation and automated driving assistance and are also heavily employed in traffic control infrastructure. Calibrating th…
Gradient-based Uncertainty for Monocular Depth Estimation
Julia Hornauer, Vasileios Belagiannis
In monocular depth estimation, disturbances in the image context, like moving objects or reflecting materials, can easily lead to erroneous predictions. For that reason, uncertaint…
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning
Dung Anh Hoang, Cuong Nguyen, Belagiannis Vasileios +2
Meta-learning is an effective method to handle imbalanced and noisy-label learning, but it depends on a validation set containing randomly selected, manually labelled and balanced…
MotionMixer: MLP-based 3D Human Body Pose Forecasting
Arij Bouazizi, Adrian Holzbock, Ulrich Kressel +2
In this work, we present MotionMixer, an efficient 3D human body pose forecasting model based solely on multi-layer perceptrons (MLPs). MotionMixer learns the spatial-temporal 3D b…
A Spatio-Temporal Multilayer Perceptron for Gesture Recognition
Adrian Holzbock, Alexander Tsaregorodtsev, Youssef Dawoud +2
Gesture recognition is essential for the interaction of autonomous vehicles with humans. While the current approaches focus on combining several modalities like image features, key…
Lightweight Monocular Depth Estimation through Guided Decoding
Michael Rudolph, Youssef Dawoud, Ronja Güldenring +2
We present a lightweight encoder-decoder architecture for monocular depth estimation, specifically designed for embedded platforms. Our main contribution is the Guided Upsampling B…
Anomaly Detection in Multi-Agent Trajectories for Automated Driving
Julian Wiederer, Arij Bouazizi, Marco Troina +2
Human drivers can recognise fast abnormal driving situations to avoid accidents. Similar to humans, automated vehicles are supposed to perform anomaly detection. In this work, we p…
Learning Temporal 3D Human Pose Estimation with Pseudo-Labels
Arij Bouazizi, Ulrich Kressel, Vasileios Belagiannis
We present a simple, yet effective, approach for self-supervised 3D human pose estimation. Unlike the prior work, we explore the temporal information next to the multi-view self-su…
MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation
Anastasia Makarevich, Azade Farshad, Vasileios Belagiannis +1
The lack of sufficient annotated image data is a common issue in medical image segmentation. For some organs and densities, the annotation may be scarce, leading to poor model trai…
Self-Supervised 3D Human Pose Estimation with Multiple-View Geometry
Arij Bouazizi, Julian Wiederer, Ulrich Kressel +1
We present a self-supervised learning algorithm for 3D human pose estimation of a single person based on a multiple-view camera system and 2D body pose estimates for each view. To…
Visual Domain Adaptation for Monocular Depth Estimation on Resource-Constrained Hardware
Julia Hornauer, Lazaros Nalpantidis, Vasileios Belagiannis
Real-world perception systems in many cases build on hardware with limited resources to adhere to cost and power limitations of their carrying system. Deploying deep neural network…
Attention-based Vehicle Self-Localization with HD Feature Maps
Nico Engel, Vasileios Belagiannis, Klaus Dietmayer
We present a vehicle self-localization method using point-based deep neural networks. Our approach processes measurements and point features, i.e. landmarks, from a high-definition…
Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification
Fengbei Liu, Yu Tian, Filipe R. Cordeiro +3
The training of deep learning models generally requires a large amount of annotated data for effective convergence and generalisation. However, obtaining high-quality annotations i…
EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels
Ragav Sachdeva, Filipe R. Cordeiro, Vasileios Belagiannis +2
The efficacy of deep learning depends on large-scale data sets that have been carefully curated with reliable data acquisition and annotation processes. However, acquiring such lar…
Dynamic Occupancy Grid Mapping with Recurrent Neural Networks
Marcel Schreiber, Vasileios Belagiannis, Claudius Gläser +1
Modeling and understanding the environment is an essential task for autonomous driving. In addition to the detection of objects, in complex traffic scenarios the motion of other ro…
Point Transformer
Nico Engel, Vasileios Belagiannis, Klaus Dietmayer
In this work, we present Point Transformer, a deep neural network that operates directly on unordered and unstructured point sets. We design Point Transformer to extract local and…
Traffic Control Gesture Recognition for Autonomous Vehicles
Julian Wiederer, Arij Bouazizi, Ulrich Kressel +1
A car driver knows how to react on the gestures of the traffic officers. Clearly, this is not the case for the autonomous vehicle, unless it has road traffic control gesture recogn…
DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registration
Markus Horn, Nico Engel, Vasileios Belagiannis +2
This work addresses the problem of point cloud registration using deep neural networks. We propose an approach to predict the alignment between two point clouds with overlapping da…
Few-Shot Microscopy Image Cell Segmentation
Youssef Dawoud, Julia Hornauer, Gustavo Carneiro +1
Automatic cell segmentation in microscopy images works well with the support of deep neural networks trained with full supervision. Collecting and annotating images, though, is not…