Demo Data from SIGIR 2026 / reviewers in the wild / expert
Ulrich Krebel
dblp:226/6234
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Image recognition and object detection · 77% 3D vision · 23% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Image recognition and object detection › object detection › traffic object detection
traffic light detection |
0.3 | 1 | 2018 | The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets · ICRA 2018 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | The DriveU Traffic Light Dataset: Introduction and Comparison with Existing DatasetsabstractAutonomous driving is a topic in computer vision which has captured a great deal of attention in recent years. One key problem is the detection and state analysis of traffic lights. Even over time, very few datasets for research in this topic have been published and they vary widely in quantity and in quality. To address the complexity of traffic light recognition, we introduce the DriveU**driveU is a joint innovation center of the Daimler AG and the University of Ulm Traffic Light Dataset (DTLD), a large-scale dataset consisting of more than 230,000 annotations. All annotations are hand-labeled according to strict rules and show a high quality. Recordings were made in eleven different cities during different weather conditions. Our dataset exceeds previous traffic light datasets in size, variance, annotation quality and amount of additional sensor data. We prove the extent of our dataset by an extensive comparison with existing traffic light datasets. Miscellaneous dataset criteria are compared, illustrated and statistically analyzed. In the process, metrics to express the quality and variance of datasets are developed and verified. The dataset can be downloaded from http://traffic-light-data.de. Andreas Fregin, Julian Müller 0001, Ulrich Krebel, Klaus Dietmayer |
ICRA | 3 |