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People Tracking in RGB-D Data With On-line Boosted Target Models
 

Summary: People Tracking in RGB-D Data
With On-line Boosted Target Models
Matthias Luber Luciano Spinello Kai O. Arras
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 maximiza-
tion 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

  

Source: Arras, Kai O. - Institut für Informatik, Albert-Ludwigs-Universität Freiburg

 

Collections: Computer Technologies and Information Sciences