Demo Data from SIGIR 2026 / reviewers in the wild / expert
Nicolas A. Tsokas
dblp:57/8368
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author
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 |
Video understanding and tracking · 50% Robot navigation and mapping · 50% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Video understanding and tracking › multi-object tracking
multiple hypothesis tracking |
0.1 | 1 | 2010 | A multiple hypothesis people tracker for teams of mobile robots · ICRA 2010 |
Robotics › Robot navigation and mapping
multi-robot perception |
0.1 | 1 | 2010 | A multiple hypothesis people tracker for teams of mobile robots · ICRA 2010 |
Robotics › Robot navigation and mapping › sensor fusion
multi-sensor tracking |
0.1 | 1 | 2010 | A multiple hypothesis people tracker for teams of mobile robots · ICRA 2010 |
Computer vision › Video understanding and tracking › object tracking
person tracking |
0.1 | 1 | 2010 | A multiple hypothesis people tracker for teams of mobile robots · ICRA 2010 |
Methods — techniques the papers use, named apart from their topics
multiple hypothesis tracking · 0.1laser range finder · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | A multiple hypothesis people tracker for teams of mobile robotsabstractThis paper tackles the problem of tracking walking people with multiple moving robots equipped with laser rangefinders. We present an adaptation to the classic Multiple Hypothesis Tracking method, which allows for one-to-many associations between targets and measurements in each cycle and is thus capable of operating in a multi-sensor scenario. In the context of two experiments, the successful integration of our tracking algorithm to a dual-robot setup is assessed. Nicolas A. Tsokas, Kostas J. Kyriakopoulos |
ICRA | 1 |