Int. Conf. Intelligent Robots and Systems 2011 (IROS)
Keywords: Kinect people detection, RGB-D people detection
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 and from the depth characteristics of
the Kinect RGB-D sensor to design a robust method
to detect people in dense depth data, called Histogram
of Oriented Depths (HOD). HOD locally encodes the
direction 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 pro-
pose Combo-HOD, a RGB-D detector that combines
HOD and HOG responses. 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.
The results demonstrate the robustness of HOD and
Combo-HOD on a real-world data set collected in a
populated indoor environment.