Name
Abstract | ||
|---|---|---|
The demanding nature of human robot collaboration (HRC) in terms of robustness and efficiency requirements make object tracking a tough challenge. Instead of treating the area of HRC as an adversary, the idea is to exploit the abundance of context information available in a human robot collaboration scenario to enhance tracking. In this work, a multi object tracking system that uses context information and which is capable of tracking the 3D pose of multiple objects using RGBD data is presented. In order to showcase the importance of context and the enhancement possible for an object tracker when integrated into a cognitive architecture, several experiments are performed and evaluated. This approach is one of the first to apply and evaluate the concept of multiple object tracking in 3D to a human robot collaborative assembly process. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3029798.3038406 | HRI (Companion) |
Keywords | Field | DocType |
Multi-Object Tracking, Context Awareness, Human Robot Collaboration | Computer vision,Simulation,Computer science,Robustness (computer science),Context awareness,Exploit,Human–computer interaction,Video tracking,Artificial intelligence,Adversary,Cognitive architecture,Human–robot interaction | Conference |
ISSN | Citations | PageRank |
2167-2121 | 0 | 0.34 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sharath Chandra Akkaladevi | 1 | 0 | 0.34 |
| Matthias Plasch | 2 | 4 | 3.29 |
| Christian Eitzinger | 3 | 164 | 15.33 |
| Bernhard Rinner | 4 | 1031 | 99.55 |