1 citations · 1 across the 15 of their papers we have counts for
16 papers
Unified Multi-Layer Subspace Modeling for Cross-Domain OOD Detection
Gerhard Krumpl, Henning Avenhaus, Horst Possegger
Out-of-Distribution (OOD) detection remains a fundamental challenge for neural networks, whose predictions can be overconfident on inputs that deviate from the training distributio…
DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction
Alexander Prutsch, David Schinagl, Horst Possegger
Advanced automation is a key technology for enhancing the safety of ground operations amidst the increasing density of commercial air traffic. While motion forecasting is a well-st…
STEP: Score-Based Temporal Energy for Human Pose Video Anomaly Detection
Jakub Micorek, Mateusz Koziński, Horst Possegger
Skeleton-based Video Anomaly Detection (VAD) offers a robust, privacy-preserving solution for identifying abnormal behaviors. To model the distribution of normal static and moving…
Learn to Rank: Visual Attribution by Learning Importance Ranking
David Schinagl, Christian Fruhwirth-Reisinger, Alexander Prutsch +2
Interpreting the decisions of complex computer vision models is crucial to establish trust and accountability, especially in safety-critical domains. An established approach to int…
SHARP: Short-Window Streaming for Accurate and Robust Prediction in Motion Forecasting
Alexander Prutsch, Christian Fruhwirth-Reisinger, David Schinagl +1
In dynamic traffic environments, motion forecasting models must be able to accurately estimate future trajectories continuously. Streaming-based methods are a promising solution, b…
ASCENT: Transformer-Based Aircraft Trajectory Prediction in Non-Towered Terminal Airspace
Alexander Prutsch, David Schinagl, Horst Possegger
Accurate trajectory prediction can improve General Aviation safety in non-towered terminal airspace, where high traffic density increases accident risk. We present ASCENT, a lightw…
Streaming Real-Time Trajectory Prediction Using Endpoint-Aware Modeling
Alexander Prutsch, David Schinagl, Horst Possegger
Future trajectories of neighboring traffic agents have a significant influence on the path planning and decision-making of autonomous vehicles. While trajectory forecasting is a we…
One Model, Many Behaviors: Training-Induced Effects on Out-of-Distribution Detection
Gerhard Krumpl, Henning Avenhaus, Horst Possegger
Out-of-distribution (OOD) detection is crucial for deploying robust and reliable machine-learning systems in open-world settings. Despite steady advances in OOD detectors, their in…
ICONIC-444: A 3.1-Million-Image Dataset for OOD Detection Research
Gerhard Krumpl, Henning Avenhaus, Horst Possegger
Current progress in out-of-distribution (OOD) detection is limited by the lack of large, high-quality datasets with clearly defined OOD categories across varying difficulty levels…
GBlobs: Local LiDAR Geometry for Improved Sensor Placement Generalization
Dušan Malić, Christian Fruhwirth-Reisinger, Alexander Prutsch +3
This technical report outlines the top-ranking solution for RoboSense 2025: Track 3, achieving state-of-the-art performance on 3D object detection under various sensor placements.…
Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference Speed
Alexander Prutsch, Horst Bischof, Horst Possegger
For efficient and safe autonomous driving, it is essential that autonomous vehicles can predict the motion of other traffic agents. While highly accurate, current motion prediction…
Vision-Language Guidance for LiDAR-based Unsupervised 3D Object Detection
Christian Fruhwirth-Reisinger, Wei Lin, Dušan Malić +2
Accurate 3D object detection in LiDAR point clouds is crucial for autonomous driving systems. To achieve state-of-the-art performance, the supervised training of detectors requires…
Into the Fog: Evaluating Robustness of Multiple Object Tracking
Nadezda Kirillova, M. Jehanzeb Mirza, Horst Bischof +1
State-of-the-art Multiple Object Tracking (MOT) approaches have shown remarkable performance when trained and evaluated on current benchmarks. However, these benchmarks primarily c…
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
Jakub Micorek, Horst Possegger, Dominik Narnhofer +2
We propose a novel approach to video anomaly detection: we treat feature vectors extracted from videos as realizations of a random variable with a fixed distribution and model this…
Identifying and Extracting Pedestrian Behavior in Critical Traffic Situations
Martin Schachner, Bernd Schneider, Fabian Weissenbacher +4
A better understanding of interactive pedestrian behavior in critical traffic situations is essential for the development of enhanced pedestrian safety systems. Real-world traffic…
Robust Localization of Key Fob Using Channel Impulse Response of Ultra Wide Band Sensors for Keyless Entry Systems
Abhiram Kolli, Filippo Casamassima, Horst Possegger +1
Using neural networks for localization of key fob within and surrounding a car as a security feature for keyless entry is fast emerging. In this paper we study: 1) the performance…