326 citations · 413 across the 11 of their papers we have counts for
14 papers
SpaRC: Sparse Radar-Camera Fusion for 3D Object Detection
Philipp Wolters, Johannes Gilg, Torben Teepe +3
In this work, we present SpaRC, a novel Sparse fusion transformer for 3D perception that integrates multi-view image semantics with Radar and Camera point features. The fusion of r…
Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking
Fabian Herzog, Johannes Gilg, Philipp Wolters +2
Accurate online multiple-camera vehicle tracking is essential for intelligent transportation systems, autonomous driving, and smart city applications. Like single-camera multiple-o…
Lifting Multi-View Detection and Tracking to the Bird's Eye View
Torben Teepe, Philipp Wolters, Johannes Gilg +2
Taking advantage of multi-view aggregation presents a promising solution to tackle challenges such as occlusion and missed detection in multi-object tracking and detection. Recent…
Unleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D Perception
Philipp Wolters, Johannes Gilg, Torben Teepe +4
Low-cost, vision-centric 3D perception systems for autonomous driving have made significant progress in recent years, narrowing the gap to expensive LiDAR-based methods. The primar…
EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye View
Torben Teepe, Philipp Wolters, Johannes Gilg +2
Multi-view aggregation promises to overcome the occlusion and missed detection challenge in multi-object detection and tracking. Recent approaches in multi-view detection and 3D ob…
Do We Still Need Non-Maximum Suppression? Accurate Confidence Estimates and Implicit Duplication Modeling with IoU-Aware Calibration
Johannes Gilg, Torben Teepe, Fabian Herzog +2
Object detectors are at the heart of many semi- and fully autonomous decision systems and are poised to become even more indispensable. They are, however, still lacking in accessib…
Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual Cities
Fabian Herzog, Junpeng Chen, Torben Teepe +3
Smart City applications such as intelligent traffic routing or accident prevention rely on computer vision methods for exact vehicle localization and tracking. Due to the scarcity…
Face Morphing: Fooling a Face Recognition System Is Simple!
Stefan Hörmann, Tianlin Kong, Torben Teepe +3
State-of-the-art face recognition (FR) approaches have shown remarkable results in predicting whether two faces belong to the same identity, yielding accuracies between 92% and 100…
Towards a Deeper Understanding of Skeleton-based Gait Recognition
Torben Teepe, Johannes Gilg, Fabian Herzog +2
Gait recognition is a promising biometric with unique properties for identifying individuals from a long distance by their walking patterns. In recent years, most gait recognition…
The Box Size Confidence Bias Harms Your Object Detector
Johannes Gilg, Torben Teepe, Fabian Herzog +1
Countless applications depend on accurate predictions with reliable confidence estimates from modern object detectors. It is well known, however, that neural networks including obj…
Lightweight Multi-Branch Network for Person Re-Identification
Fabian Herzog, Xunbo Ji, Torben Teepe +3
Person Re-Identification aims to retrieve person identities from images captured by multiple cameras or the same cameras in different time instances and locations. Because of its i…
GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition
Torben Teepe, Ali Khan, Johannes Gilg +3
Gait recognition is a promising video-based biometric for identifying individual walking patterns from a long distance. At present, most gait recognition methods use silhouette ima…
Dissected 3D CNNs: Temporal Skip Connections for Efficient Online Video Processing
Okan Köpüklü, Stefan Hörmann, Fabian Herzog +2
Convolutional Neural Networks with 3D kernels (3D-CNNs) currently achieve state-of-the-art results in video recognition tasks due to their supremacy in extracting spatiotemporal fe…
Comparative Analysis of CNN-based Spatiotemporal Reasoning in Videos
Okan Köpüklü, Fabian Herzog, Gerhard Rigoll
Understanding actions and gestures in video streams requires temporal reasoning of the spatial content from different time instants, i.e., spatiotemporal (ST) modeling. In this sur…