@InCollection{hoyninge10ccis,
author = {Nicolai v. Hoyningen-Huene and Michael Beetz},
editor = {AlpeshKumar Ranchordas and Helder Araujo},
booktitle = {VISIGRAPP 2009},
title = {Importance Sampling as One Solution to the Data Association Problem in Multi-target Tracking},
publisher = {Springer-Verlag Berlin Heidelberg},
year = {2010},
number = {68},
series = {Communications in Computer and Information Science (CCIS)},
pages = {309--325},
bib2html_pubtype = {Selected Paper},
bib2html_rescat = {Person Tracking},
bib2html_groups = {Aspogamo},
bib2html_funding = {ASpoGAMo},
bib2html_domain = {Soccer Analysis},
abstract = {Tracking multiple targets with similar appearance is a common task in many computer vision applications as surveillance or sports analysis. We propose a Rao-Blackwellized Resampling Particle Filter (RBRPF) as a real-time multi-target tracking method that solves the data association problem by a Monte Carlo approach. Each particle containing the whole target configuration is predicted by using a process model and resampled by sampling associations and fusing of the predicted state with the assigned measurement(s) instead of the common dispersion. As each target state is modeled as a Gaussian, Rao-Blackwellization can be used to solve some of these steps analytically. The sampling of associations splits the multi-target tracking problem in multiple single target tracking problems, which can be handled by Kalman filters in an optimal way. The method is independent of the order of measurements which is mostly predetermined by the measuring process in contrast to other state-of-the-art approaches. Smart resampling and memoization is introduced to equip the tracking method with real-time capabilities in the first place exploiting the discreteness of the associations. The probabilistic framework allows for consideration of appearance models and the fusion of different sensors. A way to constrain the multiplicity of measurements associated with a single target is proposed and -- along with the ability to cope with a high number of targets in clutter -- evaluated in a simulation experiment. We demonstrate the applicability of the proposed method to real world applications by tracking soccer players captured by multiple cameras through occlusions in real-time.}
}
The research project 'Sensor-based, Automatic Analysis of Football Games' is an ambitious, mid-term research project that studies the automation of these tasks. The main objectives of the project are (1) the investigation of novel computational mechanisms that enable computer systems to recognize intentional activities, (2) the development of an integrated software system to automate game interpretation and analysis, and (3) the demonstration of the impact of automated game analysis on application areas, such as sport science, football coaching, and sports entertainment.