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 C-4 and histogram of oriented gradients (HOG). ; This work was supported by Kutxa Obra Social in the project, KtBot. Work partially funded by the Institute of Intelligent Systems and Numerical Applications in Engineering (SIANI) and the Computer Science Department at ULPGC. The Basque Government Research Team grant and the University of the Basque Country UPV/EHU, under grant UFI11/45 (BAILab) are acknowledged.


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    Titel :

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



    Erscheinungsdatum :

    2013-10-29


    Anmerkungen:

    doi:10.3390/s131114687



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




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