Watch their moves: applying probabilistic multiple object tracking to autonomous robot soccer

Authors:
Thorsten Schmitt;Michael Beetz;Robert Hanek;Sebastian Buck
Affiliations:
Munich University of Technology, Department of Computer Science, D-80290 Muenchen;Munich University of Technology, Department of Computer Science, D-80290 Muenchen;Munich University of Technology, Department of Computer Science, D-80290 Muenchen;Munich University of Technology, Department of Computer Science, D-80290 Muenchen
Venue:
Eighteenth national conference on Artificial intelligence
Year:
2002

Citing 4
Cited 5

Tracking and data association

Tracking and data association
An Efficient Implementation of Reid's Multiple Hypothesis Tracking Algorithm and Its Evaluation for the Purpose of Visual Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
The AGILO autonomous robot soccer team: computational principles, experiences, and perspectives

Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Vehicles capable of dynamic vision

IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2

Plan-Based Control for Autonomous Soccer Robots (Preliminary Report)

Revised Papers from the International Seminar on Advances in Plan-Based Control of Robotic Agents,
The AGILO Robot Soccer Team—Experience-Based Learning and Probabilistic Reasoning in Autonomous Robot Control

Autonomous Robots
Camera-based observation of football games for analyzing multi-agent activities

AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Increasing multi-trackers robustness with a segmentation algorithm

ISPRA'06 Proceedings of the 5th WSEAS International Conference on Signal Processing, Robotics and Automation
Visual robot detection in robocup using neural networks

RoboCup 2004

Quantified Score

Hi-index	0.00

Visualization

Abstract

In many autonomous robot applications robots must be capable of estimating the positions and motions of moving objects in their environments. In this paper, we apply probabilistic multiple object tracking to estimating the positions of opponent players in autonomous robot soccer. We extend an existing tracking algorithm to handle multiple mobile sensors with uncertain positions, discuss the specification of probabilistic models needed by the algorithm, and describe the required vision-interpretation algorithms. The multiple object tracking has been successfully applied throughout the RoboCup 2001 world championship.