%0 Journal Article
%T On the Use of a Low-Cost Thermal Sensor to Improve Kinect People Detection in a Mobile Robot
%A Loreto Susperregi
%A Basilio Sierra
%A Modesto Castrill¨®n
%A Javier Lorenzo
%A Jose Mar¨ªa Mart¨ªnez-Otzeta
%A Elena Lazkano
%J Sensors
%D 2013
%I MDPI AG
%R 10.3390/s131114687
%X Detecting people is a key capability for robots that operate in populated environments. In this paper, we have adopted a hierarchical approach that combines classifiers created using supervised learning in order to identify whether a person is in the view-scope of the robot or not. Our approach makes use of vision, depth and thermal sensors mounted on top of a mobile platform. The set of sensors is set up combining the rich data source offered by a Kinect sensor, which provides vision and depth at low cost, and a thermopile array sensor. Experimental results carried out with a mobile platform in a manufacturing shop floor and in a science museum have shown that the false positive rate achieved using any single cue is drastically reduced. The performance of our algorithm improves other well-known approaches, such as C4 and histogram of oriented gradients (HOG).
%K sensor fusion
%K people detection
%K computer vision
%K hierarchical classification
%K mobile robot/platform
%U http://www.mdpi.com/1424-8220/13/11/14687