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People tracking in rgb-d data with on-line boosted target models (2011)
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| Venue: | In IEEE/RSJ Int. Conf. on |
| Citations: | 18 - 1 self |
BibTeX
@INPROCEEDINGS{Luber11peopletracking,
author = {Matthias Luber and Luciano Spinello and Kai O. Arras},
title = {People tracking in rgb-d data with on-line boosted target models},
booktitle = {In IEEE/RSJ Int. Conf. on},
year = {2011}
}
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Abstract
Abstract — People tracking is a key component for robots that are deployed in populated environments. Previous works have used cameras and 2D and 3D range finders for this task. In this paper, we present a 3D people detection and tracking approach using RGB-D data. We combine a novel multi-cue person detector for RGB-D data with an on-line detector that learns individual target models. The two detectors are integrated into a decisional framework with a multi-hypothesis tracker that controls on-line learning through a track interpretation feedback. For on-line learning, we take a boosting approach using three types of RGB-D features and a confidence maximization search in 3D space. The approach is general in that it neither relies on background learning nor a ground plane assumption. For the evaluation, we collect data in a populated indoor environment using a setup of three Microsoft Kinect sensors with a joint field of view. The results demonstrate reliable 3D tracking of people in RGB-D data and show how the framework is able to avoid drift of the on-line detector and increase the overall tracking performance. I.
Keyphrases
rgb-d data on-line boosted target model on-line detector joint field multi-hypothesis tracker key component decisional framework populated indoor environment range finder people detection confidence maximization search previous work track interpretation feedback novel multi-cue person detector boosting approach rgb-d feature abstract people tracking microsoft kinect sensor on-line learning individual target model ground plane assumption overall tracking performance populated environment
