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People Detection in RGB-D Data Luciano Spinello Kai O. Arras
 

Summary: People Detection in RGB-D Data
Luciano Spinello Kai O. Arras
Abstract-- People detection is a key issue for robots
and intelligent systems sharing a space with people.
Previous works have used cameras and 2D or 3D range
finders for this task. In this paper, we present a novel
people detection approach for RGB-D data. We take
inspiration from the Histogram of Oriented Gradients
(HOG) detector to design a robust method to detect
people in dense depth data, called Histogram of Ori-
ented Depths (HOD). HOD locally encodes the direc-
tion of depth changes and relies on an depth-informed
scale-space search that leads to a 3-fold acceleration
of the detection process. We then propose Combo-
HOD, a RGB-D detector that probabilistically com-
bines HOD and HOG. The experiments include a
comprehensive comparison with several alternative
detection approaches including visual HOG, several
variants of HOD, a geometric person detector for 3D
point clouds, and an Haar-based AdaBoost detector.

  

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

 

Collections: Computer Technologies and Information Sciences