On the use of a low-cost thermal sensor to improve Kinect people detection in a mobile robot

Sensors (Basel). 2013 Oct 29;13(11):14687-713. doi: 10.3390/s131114687.


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).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Databases, Factual
  • Environmental Monitoring / instrumentation*
  • Environmental Monitoring / methods
  • Humans
  • Image Processing, Computer-Assisted / instrumentation*
  • Image Processing, Computer-Assisted / methods
  • Reproducibility of Results
  • Robotics / instrumentation*
  • Support Vector Machine
  • Thermography / instrumentation*
  • Thermography / methods