On the Use of a Low-Cost Thermal Sensor to Improve Kinect People Detection in a Mobile Robot

Sensors

Loreto Susperregi³  Basilio Sierra²  Modesto Castrillón¹  Javier Lorenzo¹  Jose Mar໚ Martínez-Otzeta³ 

[1] SIANI, Universidad de Las Palmas de Gran Canaria, Juan de Quesada 30, Spain; E-Mails:;Department of Computer Science and Artificial Intelligence, UPV-EHU, Manuel Lardizabal 1, Donostia-San Sebastin, Spain; E-Mails:;Autonomous and Smart Systems Unit, IK4-TEKNIKER, Iaki Goenaga 5, Eibar, Spain; E-Mail:

关键词: sensor fusion;    people detection;    computer vision;    hierarchical classification;    mobile robot/platform;   

DOI  :  10.3390/s131114687

来源: mdpi

【 摘 要 】

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).

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.